Public Health Surveillance
Public Health Surveillance datasets in the CDC Open Data Catalog
This page contains all datasets in the Public Health Surveillance category of the CDC Open Data Catalog.
Total Datasets in Category: 66 Last Updated: 07/14/2025
Datasets in this Category
1998-2023 Serotype Data for Invasive Pneumococcal Disease Cases by Age Group from Active Bacterial Core surveillance
Description: CDC monitors invasive bacterial infections that cause bloodstream infections, sepsis, and meningitis in persons living in the community through Active Bacterial Core surveillance (ABCs). ABCs conducts laboratory- and population-based surveillance for invasive pneumococcal disease (IPD). ABCs serotype data are used to measure the impact of vaccine use in the United States on vaccine-type IPD. This table reports IPD case counts in the ABCs catchment area by serotype for years 1998 through 2022. Cases are grouped into the following mutually exclusive age groups: age <2 years old, age 2–4 years old, age 5–17 years old, age 18–49 years old, age 50–64 years old, and age ≥65 years old. ABCs methods and surveillance areas reporting IPD cases has changed over time. Given these changes, trends in serotype distribution by year and age group should be interpreted with caution. Additional information on ABCs methods and surveillance population is available at https://www.cdc.gov/abcs/methodology/index.html. Analyze and visualize data using the ABCs Bact Facts Interactive Data Dashboard at https://www.cdc.gov/abcs/bact-facts-interactive-dashboard.
Schema: dwv_pub_health_surv
Table Name: abcs_pneumococcal_serotype_data_1998_20__qvzb_qs6p
Dataset ID: qvzb-qs6p
Category: Public Health Surveillance
Tags: abcs, active bacterial core surveillance, bactfacts, invasive pneumococcal disease serotypes, ipd serotype frequencies, ipd serotypes, spn serotypes, streptococcus pneumoniae serotypes
Source Data: https://data.cdc.gov/d/qvzb-qs6p
Active Bacterial Core surveillance (ABCs) Group B Streptococcus
Description: ABCs is an ongoing surveillance program that began in 1997. ABCs reports describe the ABCs case definition and the specific methodology used to calculate rates and estimated numbers in the United States for each bacterium by year. The methods, surveillance areas, and laboratory isolate collection areas have changed over time. Additionally, the way missing data are taken into account changed in 2010. It went from distributing unknown values based on known values of cases by site to use of multiple imputation using a sequential regression imputation method. Given these changes over time, trends should be interpreted with caution.
Methodology Find details about surveillance population, case determination, surveillance evaluation, and more.
Reports and Findings Get official interpretations from reports and publications created from ABCs data.
Schema: dwv_pub_health_surv
Table Name: abc_groupb_streptococcus_ere_s_the_conv__95m5_agj4
Dataset ID: 95m5-agj4
Category: Public Health Surveillance
Tags: abcs, bactfacts
Source Data: https://data.cdc.gov/d/95m5-agj4
Active Bacterial Core surveillance (ABCs) Group A Streptococcus
Description: ABCs is an ongoing surveillance program that began in 1997. ABCs reports describe the ABCs case definition and the specific methodology used to calculate rates and estimated numbers in the United States for each bacterium by year. The methods, surveillance areas, and laboratory isolate collection areas have changed over time. Additionally, the way missing data are taken into account changed in 2010. It went from distributing unknown values based on known values of cases by site to use of multiple imputation using a sequential regression imputation method. Given these changes over time, trends should be interpreted with caution.
Methodology Find details about surveillance population, case determination, surveillance evaluation, and more.
Reports and Findings Get official interpretations from reports and publications created from ABCs data.
Schema: dwv_pub_health_surv
Table Name: active_bacterial_core_surveillance_grou__9y49_tura
Dataset ID: 9y49-tura
Category: Public Health Surveillance
Tags: abcs, bactfacts
Source Data: https://data.cdc.gov/d/9y49-tura
Active Bacterial Core surveillance (ABCs) Haemophilus influenzae
Description: ABCs is an ongoing surveillance program that began in 1997. ABCs reports describe the ABCs case definition and the specific methodology used to calculate rates and estimated numbers in the United States for each bacterium by year. The methods, surveillance areas, and laboratory isolate collection areas have changed over time. Additionally, the way missing data are taken into account changed in 2010. It went from distributing unknown values based on known values of cases by site to use of multiple imputation using a sequential regression imputation method. Given these changes over time, trends should be interpreted with caution.
Methodology Find details about surveillance population, case determination, surveillance evaluation, and more.
Reports and Findings Get official interpretations from reports and publications created from ABCs data.
Schema: dwv_pub_health_surv
Table Name: active_bacterial_core_surveillance_haem__uxwq_vny5
Dataset ID: uxwq-vny5
Category: Public Health Surveillance
Tags: abcs, bactfacts
Source Data: https://data.cdc.gov/d/uxwq-vny5
Active Bacterial Core surveillance (ABCs) Neisseria meningitidis
Description:
ABCs is an ongoing surveillance program that began in 1997. ABCs reports describe the ABCs case definition and the specific methodology used to calculate rates and estimated numbers in the United States for each bacterium by year. The methods, surveillance areas, and laboratory isolate collection areas have changed over time. Additionally, the way missing data are taken into account changed in 2010. It went from distributing unknown values based on known values of cases by site to use of multiple imputation using a sequential regression imputation method. Given these changes over time, trends should be interpreted with caution.
Methodology Find details about surveillance population, case determination, surveillance evaluation, and more.
Reports and Findings Get official interpretations from reports and publications created from ABCs data.
Schema: dwv_pub_health_surv
Table Name: active_bacterial_core_surveillance_neum__8bda_nhxv
Dataset ID: 8bda-nhxv
Category: Public Health Surveillance
Tags: abcs, bactfacts
Source Data: https://data.cdc.gov/d/8bda-nhxv
Active Bacterial Core surveillance (ABCs) Streptococcus pneumoniae
Description: ABCs is an ongoing surveillance program that began in 1997. ABCs reports describe the ABCs case definition and the specific methodology used to calculate rates and estimated numbers in the United States for each bacterium by year. The methods, surveillance areas, and laboratory isolate collection areas have changed over time. Additionally, the way missing data are taken into account changed in 2010. It went from distributing unknown values based on known values of cases by site to use of multiple imputation using a sequential regression imputation method. Given these changes over time, trends should be interpreted with caution.
Methodology Find details about surveillance population, case determination, surveillance evaluation, and more.
Reports and Findings Get official interpretations from reports and publications created from ABCs data.
Schema: dwv_pub_health_surv
Table Name: active_bacterial_core_surveillance_stre__en3s_hzsr
Dataset ID: en3s-hzsr
Category: Public Health Surveillance
Tags: abcs, bactfacts
Source Data: https://data.cdc.gov/d/en3s-hzsr
Level of Acute Respiratory Illness (ARI) Activity by State
Description: Respiratory illness activity is monitored using the acute respiratory illness (ARI) metric. ARI captures a broad range of diagnoses from emergency department visits for respiratory illnesses, from the common cold to severe infections like influenza, RSV and COVID-19. It captures illnesses that may not present with fever, offering a more complete picture than the previous influenza-like illness (ILI) metric. Updated once per week on Fridays.
Schema: dwv_pub_health_surv
Table Name: ari_activity_by_state_xplanation_evel_o__f3zz_zga5
Dataset ID: f3zz-zga5
Category: Public Health Surveillance
Source Data: https://data.cdc.gov/d/f3zz-zga5
autism prevalence studies
Description: This data table provides a collection of information from peer-reviewed autism prevalence studies. Information reported from each study includes the autism prevalence estimate and additional study characteristics (e.g., case ascertainment and criteria). A PubMed search was conducted to identify studies published at any time through September 2020 using the search terms: autism (title/abstract) OR autistic (title/abstract) AND prevalence (title/abstract). Data were abstracted and included if the study fulfilled the following criteria: • The study was published in English; • The study produced at least one autism prevalence estimate; and • The study was population-based (any age range) within a defined geographic area.
Schema: dwv_pub_health_surv
Table Name: autism_prevalence_studies_his_identifie__9mw4_6adp
Dataset ID: 9mw4-6adp
Category: Public Health Surveillance
Tags: asd, autism, autism prevalence, autism spectrum disorder, prevalence studies
CDC Epidemic Trends and Rt
Description: An archive of estimated trend categories, probabilities of epidemic growth, and Rt, updated weekly for COVID-19 and flu. Estimates are based on emergency department visits reported to the National Syndromic Surveillance Program (NSSP), and generated using a model that includes nowcasting to adjust for incomplete reports on the most recent dates. See the visuals supported by these data, and more information about the data, models and methods at https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html, and https://www.cdc.gov/respiratory-viruses/data/activity-levels.html. For a semi-technical overview of the modeling methods used to generate these estimates see https://www.cdc.gov/cfa-behind-the-model/php/data-research/rt-estimates/index.html. For the code used to run the models, see https://github.com/CDCgov/cfa-epinow2-pipeline.
Schema: dwv_pub_health_surv
Table Name: cdc_epidemic_trends_and_rt_his_identifi__5dqz_y4ea
Dataset ID: 5dqz-y4ea
Category: Public Health Surveillance
Tags: cfa, coronavirus, covid-19, epidemic trend, flu, influenza, modeling, respiratory virus response, rt, rvr
Source Data: https://data.cdc.gov/d/5dqz-y4ea
Rates of COVID-19 Cases or Deaths by Age Group and Vaccination Status and Second Booster Dose
Description: Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022. Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases
Schema: dwv_pub_health_surv
Table Name: covid19_cases_deaths_age_vaccination_bo__ukww_au2k
Dataset ID: ukww-au2k
Category: Public Health Surveillance
COVID-19-related School Closures: USA, 2020-2022
Description: Unplanned public K-12 school district and individual school closures due to COVID-19 in the United States from August 1, 2020–June 30, 2022.
Schema: dwv_pub_health_surv
Table Name: covid19_school_closures_usa_2020_2022__jnru_aqxk
Dataset ID: jnru-aqxk
Category: Public Health Surveillance
Tags: community mitigation, covid-19, emergency preparedness, pandemic, pandemic preparedness
COVID-19-associated school closures, United States, February 18–June 30, 2020
Description: Unplanned public K-12 school district and individual school closures due to COVID-19 in the United States from February 18–June 30, 2020.
Schema: dwv_pub_health_surv
Table Name: covid19_school_closures_us_feb_to_jun_2__wgvr_7mvz
Dataset ID: wgvr-7mvz
Category: Public Health Surveillance
Tags: community mitigation, coronavirus, covid-19, school closure
COVID-19 Weekly Cases and Deaths by Age, Race/Ethnicity, and Sex - ARCHIVED
Description: Note: Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death. This table summarizes COVID-19 case and death data submitted to CDC as case reports for the line-level dataset. Case and death counts are stratified according to sex, age, and race and ethnicity at regional and national levels. Data for US territories are included in case and death counts, but not population counts. Weekly cumulative counts with five or fewer cases or deaths are not reported to protect confidentiality of patients. Records with unknown or missing sex, age, or race and ethnicity and of multiple, non-Hispanic race and ethnicity are included in case and death totals. COVID-19 case and death data are provisional and are subject to change. Visualization of COVID-19 case and death rate trends by demographic variables may be viewed on COVID Data Tracker (https://covid.cdc.gov/covid-data-tracker/#demographicsovertime).
Schema: dwv_pub_health_surv
Table Name: covid19_weekly_cases_deaths_age_raceeth__hrdz_jaxc
Dataset ID: hrdz-jaxc
Category: Public Health Surveillance
Tags: case, community transmission, coronavirus, county, covid-19, laboratory, ncird-corvd
Source Data: https://data.cdc.gov/d/hrdz-jaxc
2023 Respiratory Virus Response - NSSP Emergency Department Visits - COVID-19, Flu, RSV, Combined
Description: 2023 Respiratory Virus Response - NSSP Emergency Department Visits - COVID-19, Flu, RSV, Combined For additional information, please see: Companion Guide: NSSP Emergency Department Data on Respiratory Illness
Schema: dwv_pub_health_surv
Table Name: ere_is_the_clean_identifier_for_the_giv__vutn_jzwm
Dataset ID: vutn-jzwm
Category: Public Health Surveillance
Tags: coronavirus, covid19, ed, emergency department, flu, influenza, national syndromic surveillance program, ncird, nssp, ophdst, respiratory syncytial virus, respiratory virus response, rsv, rvr
Source Data: https://data.cdc.gov/d/vutn-jzwm
HAICViz - Candidemia
Description: The healthcare-associated infection component of CDC’s EIP engages a network of state health departments and their academic medical center partners to help answer critical questions about emerging HAI threats, advanced infection tracking methods, and antibiotic resistance in the United States. Information gathered through this activity will play a key role in shaping future policies and recommendations targeting HAI prevention.
Schema: dwv_pub_health_surv
Table Name: haicviz_candidemia_xplanation_iz_is_con__34p9_h4us
Dataset ID: 34p9-h4us
Category: Public Health Surveillance
Tags: haicviz
HAICViz - CDI
Description:
The healthcare-associated infection component of CDC’s EIP engages a network of state health departments and their academic medical center partners to help answer critical questions about emerging HAI threats, advanced infection tracking methods, and antibiotic resistance in the United States. Information gathered through this activity will play a key role in shaping future policies and recommendations targeting HAI prevention.
Schema: dwv_pub_health_surv
Table Name: haicviz_cdi_ere_s_the_breakdown_of_the___abgz_qs4g
Dataset ID: abgz-qs4g
Category: Public Health Surveillance
Tags: clostridioides difficile, haicviz
Source Data: https://data.cdc.gov/d/abgz-qs4g
HAICViz - iSA
Description:
The healthcare-associated infection component of CDC’s EIP engages a network of state health departments and their academic medical center partners to help answer critical questions about emerging HAI threats, advanced infection tracking methods, and antibiotic resistance in the United States. Information gathered through this activity will play a key role in shaping future policies and recommendations targeting HAI prevention. Click here to learn more about Invasive Staphylococcus aureus infections
InterpretationData presented in HAICViz may differ from other HAIC publications since different datasets or methods may be used.
Small numbers for some topics or filters may make year to year changes difficult to interpret.
Since each infection may have unique characteristics, the information available to display differs by individual organism.
More DetailsMethodology: Find details about surveillance population, case determination, surveillance evaluation, and more.
Reports and Findings: Access reports or lists of publications using HAIC Invasive Staphylococcus aureus data
Schema: dwv_pub_health_surv
Table Name: haicviz_isa_xplanation_iz_is_converted___ssz5_s49e
Dataset ID: ssz5-s49e
Category: Public Health Surveillance
Tags: mrsa, haicviz, healthcare associated infections, mssa, staphylococcus aureus
Source Data: https://data.cdc.gov/d/ssz5-s49e
HAICViz - MuGSI
Description: The healthcare-associated infection component of CDC’s EIP engages a network of state health departments and their academic medical center partners to help answer critical questions about emerging HAI threats, advanced infection tracking methods, and antibiotic resistance in the United States. Information gathered through this activity will play a key role in shaping future policies and recommendations targeting HAI prevention.
Schema: dwv_pub_health_surv
Table Name: haicviz_mugsi_xplanation_iz_is_converte__v4tm_h8pe
Dataset ID: v4tm-h8pe
Category: Public Health Surveillance
Tags: antimicrobial resistance, carbapenem-resistant acinetobacter baumannii, carbapenem-resistant enterobacterales, enterobacter spp., escherichia coli
Health Service Area Population Greater Than 2Million
Schema: dwv_pub_health_surv
Table Name: health_service_area_population_over_2mi__mu4v_zehj
Dataset ID: mu4v-zehj
Category: Public Health Surveillance
Influenza-related School Closures: USA, 2011-2022
Description: Unplanned public K-12 school district and individual school closures due to influenza and influenza-like illness in the United States from August 1, 2011–June 30, 2022.
Schema: dwv_pub_health_surv
Table Name: influenza_school_closures_usa_2011_2022__5una_zw6e
Dataset ID: 5una-zw6e
Category: Public Health Surveillance
Tags: community mitigation, emergency preparedness, influenza, pandemic preparedness, school closure
Monthly COVID-19 Death Rates per 100,000 Population by Age Group, Race and Ethnicity, Sex, and Region
Description: Monthly COVID-19 death rates per 100,000 population stratified by age group, race/ethnicity, sex, and region
Schema: dwv_pub_health_surv
Table Name: monthly_covid19_death_rates_per_100k_po__89qs_mr7i
Dataset ID: 89qs-mr7i
Category: Public Health Surveillance
Tags: coronavirus, covid-19, deaths, demographics, mortality, ncird-corvd
Source Data: https://data.cdc.gov/d/89qs-mr7i
Monthly COVID-19 Death Rates per 100,000 Population by Age Group, Race and Ethnicity, Sex, and Region with Double Stratification
Description: Monthly COVID-19 death rates per 100,000 population stratified by age group, race/ethnicity, sex, and region, with race/ethnicity by age group and age group by race/ethnicity double stratification
Schema: dwv_pub_health_surv
Table Name: monthly_covid19_death_rates_per_100k_po__exs3_hbne
Dataset ID: exs3-hbne
Category: Public Health Surveillance
Tags: sex, race and ethnicity, ncird-corvd, mortality, deaths, covid-19, coronavirus, age group, age-adjusted
Source Data: https://data.cdc.gov/d/exs3-hbne
Monthly Rates of Laboratory-Confirmed COVID-19 Hospitalizations from the COVID-NET Surveillance System
Description: The Coronavirus Disease 2019 (COVID-19) Hospitalization Surveillance Network (COVID-NET) a network that conducts active, population-based surveillance for laboratory-confirmed COVID-19-associated hospitalizations among children and adults. COVID-NET, along with the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET) and the Influenza Hospitalization Surveillance Network (FluSurv-NET), comprise the Respiratory Virus Hospitalization Surveillance Network (RESP-NET). The RESP-NET platforms have overlapping surveillance areas and use similar methods to collect data. COVID-NET is CDC’s source for important data on rates of hospitalizations associated with COVID-19. Hospitalization rates show how many people in the surveillance area are hospitalized with COVID-19, compared to the total number of people residing in that area. Data are preliminary and subject to change as more data become available. Data will be updated weekly.
Schema: dwv_pub_health_surv
Table Name: monthly_rates_covid19_hospitalizations___cf5u_bm9w
Dataset ID: cf5u-bm9w
Category: Public Health Surveillance
Tags: respiratory disease, respiratory virus response, rates by age group, rates by race and ethnicity, respiratory illness, resp-net, surveillance, respnet, respiratory-virus-response, covid, covid19, covid-19, covidnet, covid-net, hospitalization rate, hospitalizations, rate
Source Data: https://data.cdc.gov/d/cf5u-bm9w
NSSP Emergency Department Visits - COVID-19, Flu, RSV, Combined – by Demographic Category
Description: NSSP Emergency Department Visits - COVID-19, Flu, RSV, Combined – by Demographic Category For additional information, please see: Companion Guide: NSSP Emergency Department Data on Respiratory Illness. Updated once per week on Fridays.
Schema: dwv_pub_health_surv
Table Name: nssp_ed_visits_covid_flu_rsv_demographi__7xva_uux8
Dataset ID: 7xva-uux8
Category: Public Health Surveillance
Tags: coronavirus, covid19, ed, emergency department, flu, influenza, national syndromic surveillance program, ncird, nssp, ophdst, respiratory syncytial, respiratory syncytial virus, rsv, rvr, virus
Source Data: https://data.cdc.gov/d/7xva-uux8
2023 Respiratory Virus Response - NSSP Emergency Department Visit Trajectories by State- COVID-19, Flu, RSV, Combined
Description: 2023 Respiratory Viruses Response – National Syndromic Surveillance Program Emergency Department Visit Trajectories - COVID-19, Flu, RSV, Combined – by state. This dataset provides the percentage of emergency department patient visits for the specified pathogen of all ED patient visits for the specified geography that were observed for the given week from data submitted to the National Syndromic Surveillance Program (NSSP). In addition, the trend over time both as of the given row date and as of the most current data submitted is characterized as increasing, decreasing or stable to provide awareness of how the weekly trend is changing for the given geographic region. For the emergency department time series, trajectory classifications reported on the opening page are based on rolling regression model assessments of the slope for each respiratory illness. Weeks with a significant time term (p <0.05) are classified as increasing when the slope is positive and decreasing when the slope is negative. Weeks with a non-significant time term (p ≥ 0.05) are classified as stable. A 3-week moving average is applied to the time series prior to the regression procedure in order to smooth week-to-week variation. For additional information, please see:Companion Guide: NSSP Emergency Department Data on Respiratory Illness Updated once per week on Fridays.
Schema: dwv_pub_health_surv
Table Name: nssp_ed_visit_trajectories_by_state_202__7mra_9cq9
Dataset ID: 7mra-9cq9
Category: Public Health Surveillance
Tags: coronavirus, covid19, ed, emergency-department, flu
NSSP Emergency Department Visit Trajectories by State and Sub State Regions- COVID-19, Flu, RSV, Combined
Description: NSSP Emergency Department (ED) Visit Trajectories by State and Sub-State Regions- COVID-19, Flu, RSV, Combined. This dataset provides the percentage of emergency department patient visits for the specified pathogen of all ED patient visits for the specified geographic part of the country that were observed for the given week from data submitted to the National Syndromic Surveillance Program (NSSP). In addition, the trend over time is characterized as increasing, decreasing or no change, with exceptions for when there are no data available, the data are too sparse, or there are not enough data to compute a trend. These data are to provide awareness of how the weekly trend is changing for the given geographic region. Note that the reported sub-state trends are from Health Service Areas (HSA) and the data reported from the health care facilities located within the given HSA. Health Service Areas are regions of one or more counties that align to patterns of care seeking. The HSA level data are reported for each county in the HSA. More information on HSAs is available here. For the emergency department time series, trajectory classifications reported on for sub-state (HSA) emergency department time series, trajectory classifications are based on approximations of the first derivative (slope) of trends that are smoothed using generalized additive models (GAMs). To determine time intervals in which the slope is sufficiently changing (i.e., rate of change distinguishable from 0), 95% confidence intervals for the slope approximations are calculated and assessed. Weeks with a 95% confidence interval not containing 0 are classified as increasing if the slope estimate is positive and decreasing if the slope estimate is negative. Weeks with a 95% confidence interval containing 0 are classified as stable. In the scenario that an HSA's time series is determined to be too sparse (i.e., many weeks with percentages of 0%), a model is not fit, and the HSA is classified as “sparse”. For additional information, please see: Companion Guide: NSSP Emergency Department Data on Respiratory Illness Updated once per week on Fridays.
Schema: dwv_pub_health_surv
Table Name: nssp_ed_visit_trajectories_state_substa__rdmq_nq56
Dataset ID: rdmq-nq56
Category: Public Health Surveillance
Tags: coronavirus, covid19, ed, emergency department, flu, influenza, national syndromic surveillance program, ncird, nssp, ophdst, respiratory syncytial virus, rsv, rvr
Source Data: https://data.cdc.gov/d/rdmq-nq56
Percent Positivity of Viral Detections Among Enrolled Children in the New Vaccine Surveillance Network (NVSN), Acute Respiratory Illnesses (ARI), 2017–Present
Description: Percent positivity of 9 viral pathogens, by season and age group, 2017–Present.
Schema: dwv_pub_health_surv
Table Name: nvsn_positivity_viraldetections_ari_201__kipu_qxy8
Dataset ID: kipu-qxy8
Category: Public Health Surveillance
Tags: adenovirus, covid-19, enterovirus, evd-68, human coronaviruses, human metapneumovirus, influenza, medically attended illness, ncird-corvd, parainfluenza virus, pediatric, respiratory illness, rhinovirus, rsv, viral detections
Source Data: https://data.cdc.gov/d/kipu-qxy8
NWSS Public SARS-CoV-2 Concentration in Wastewater Data
Description: This dataset provides a complete time history of SARS-CoV-2 concentrations in wastewater for each sampling location.
Schema: dwv_pub_health_surv
Table Name: nwss_public_sars_cov2_wastewater_data__g653_rqe2
Dataset ID: g653-rqe2
Category: Public Health Surveillance
Tags: wastewater
Source Data: https://data.cdc.gov/d/g653-rqe2
NWSS Public SARS-CoV-2 Wastewater Metric Data
Description: This dataset provides a complete time history of metrics calculated using SARS-CoV-2 concentrations in wastewater.
Schema: dwv_pub_health_surv
Table Name: nwss_public_sars_cov_2_wastewater_metri__2ew6_ywp6
Dataset ID: 2ew6-ywp6
Category: Public Health Surveillance
Tags: covid19, sars-cov-2, wastewater
Source Data: https://data.cdc.gov/d/2ew6-ywp6
Outpatient Respiratory Illness Activity Map
Description: This dataset has been archived and will no longer be updated as of 10/16/2024. For updated data, please refer to the ILINet State Activity Indicator Map. Information on outpatient visits to health care providers for respiratory illness referred to as influenza-like illness (ILI) is collected through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). ILINet consists of outpatient healthcare providers in all 50 states, Puerto Rico, the District of Columbia, and the U.S. Virgin Islands. More than 100 million patient visits were reported during the 2022-23 season. Each week, more than 3,000 outpatient health care providers around the country report to CDC the number of patient visits for ILI by age group (0-4 years, 5-24 years, 25-49 years, 50-64 years, and ≥65 years) and the total number of visits for any reason. A subset of providers also reports total visits by age group. For this system, ILI is defined as fever (temperature of 100°F [37.8°C] or greater) and a cough and/or a sore throat. Activity levels are based on the percent of outpatient visits due to ILI in a jurisdiction compared to the average percent of ILI visits that occur during weeks with little or no influenza virus circulation (non-influenza weeks) in that jurisdiction. The number of sites reporting each week is variable; therefore, baselines are adjusted each week based on which sites within each jurisdiction provide data. To perform this adjustment, provider level baseline ILI ratios are calculated for those that have a sufficient reporting history. Providers that do not have the required reporting history to calculate a provider-specific baseline are assigned the baseline ratio for their practice type. The jurisdiction level baseline is then calculated using a weighted sum of the baseline ratios for each contributing provider. The activity levels compare the mean reported percent of visits due to ILI during the current week to the mean reported percent of visits due to ILI during non-influenza weeks. The 13 activity levels correspond to the number of standard deviations below, at, or above the mean for the current week compared with the mean during non-influenza weeks. Activity levels are classified as minimal (levels 1-3), low (levels 4-5), moderate (levels 6-7), high (levels 8-10), and very high (levels 11-13). An activity level of 1 corresponds to an ILI percentage below the mean, level 2 corresponds to an ILI percentage less than 1 standard deviation above the mean, level 3 corresponds to an ILI percentage more than 1 but less than 2 standard deviations above the mean, and so on, with an activity level of 10 corresponding to an ILI percentage 8 to 11 standard deviations above the mean. The very high levels correspond to an ILI percentage 12 to 15 standard deviations above the mean for level 11, 16 to 19 standard deviations above the mean for level 12, and 20 or more standard deviations above the mean for level 13. Disclaimers: The ILI Activity Indicator map reflects the intensity of ILI activity, not the extent of geographic spread of ILI, within a jurisdiction. Therefore, outbreaks occurring in a single area could cause the entire jurisdiction to display high or very high activity levels. In addition, data collected in ILINet may disproportionally represent certain populations within a jurisdiction, and therefore, may not accurately depict the full picture of respiratory illness activity for the entire jurisdiction. Differences in the data presented here by CDC and independently by some health departments likely represent differing levels of data completeness with data presented by the health department likely being more complete. More information is available on FluView Interactive.
Schema: dwv_pub_health_surv
Table Name: outpatient_respiratory_illness_activity__6svj_q4zv
Dataset ID: 6svj-q4zv
Category: Public Health Surveillance
Tags: archive, ncird, ncird-id, respiratory-virus-response
Pathogen Detections Among Enrolled Children in the New Vaccine Surveillance Network (NVSN), Acute Respiratory Illnesses (ARI), 12 Month Rolling Period
Description: Percent positivity of 9 viral pathogens, and enrollment counts of children with ARI by week for the past 12 months (rolling x-axis).
Schema: dwv_pub_health_surv
Table Name: pathogen_detections_nvsn_ari_12month_ro__r229_z6ma
Dataset ID: r229-z6ma
Category: Public Health Surveillance
Tags: adenovirus, covid-19, enterovirus, evd-68, human coronaviruses, human metapneumovirus, influenza, medically attended illness, ncird-corvd, parainfluenza virus, pediatric, respiratory illness, rhinovirus, rsv, viral detections
Source Data: https://data.cdc.gov/d/r229-z6ma
Patient Characteristics of Laboratory-Confirmed COVID-19 Hospitalizations from the COVID-NET Surveillance System
Description: The Coronavirus Disease 2019 (COVID-19) Hospitalization Surveillance Network (COVID-NET) a network that conducts active, population-based surveillance for laboratory-confirmed COVID-19-associated hospitalizations among children and adults. COVID-NET, along with the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET) and the Influenza Hospitalization Surveillance Network (FluSurv-NET), comprise the Respiratory Virus Hospitalization Surveillance Network (RESP-NET). The RESP-NET platforms have overlapping surveillance areas and use similar methods to collect data. COVID-NET is CDC’s source for important data on rates of hospitalizations associated with COVID-19. Hospitalization rates show how many people in the surveillance area are hospitalized with COVID-19, compared to the total number of people residing in that area. Data are preliminary and subject to change as more data become available. Data will be updated weekly.
Schema: dwv_pub_health_surv
Table Name: patient_covid19_hospital_characteristic__bigw_pgk2
Dataset ID: bigw-pgk2
Category: Public Health Surveillance
Tags: percent mechanically ventilated, covid, covid19, surveillance, percent in-hospital death, percent icu, in-hospital death, covid-19, covidnet, icu, covid-net, epi curve, resp-net, respnet, respiratory virus response, respiratory illness, hospitalizations
Source Data: https://data.cdc.gov/d/bigw-pgk2
Percent of Tests Positive for Viral Respiratory Pathogens
Description: Percent of tests positive for a pathogen is one of the surveillance metrics used to monitor respiratory pathogen transmission over time. The percent of tests positive is calculated by dividing the number of positive tests by the total number of tests administered, then multiplying by 100 [(# of positive tests/total tests) x 100]. These data include percent of tests positive values for the detection of severe acute respiratory virus coronavirus type 2 (SARS-CoV-2), the virus that causes COVID-19 and Respiratory syncytial virus (RSV) reported to the National Respiratory and Enteric Virus Surveillance System (NREVSS), a sentinel network of laboratories located through the US, includes clinical, public health and commercial laboratories; additional information available at: https://www.cdc.gov/surveillance/nrevss/index.html. Influenza results include clinical laboratory test results from NREVSS and influenza collaborating laboratories; more details about influenza virologic surveillance are available here: https://www.cdc.gov/flu/weekly/overview.html. Data represent calculations based on laboratory tests performed, not individual people tested. RSV and COVID-19 are limited to nucleic acid amplification tests (NAATs), also listed as polymerase chain reaction tests (PCR). Participating laboratories report weekly to CDC the total number of RSV tests performed that week and the number of those tests that were positive. The RSV trend graphs display the national average of the weekly % test positivity for the current, previous, and following weeks in accordance with the recommendations for assessing RSV trends by percent (https://academic.oup.com/jid/article/216/3/345/3860464). All data are provisional and subject to change.
Schema: dwv_pub_health_surv
Table Name: percent_of_tests_positive_viral_respira__seuz_s2cv
Dataset ID: seuz-s2cv
Category: Public Health Surveillance
Tags: coronavirus, covid19, flu, influenza, ncird, ncird-corvd, ncird-id, rsv
Source Data: https://data.cdc.gov/d/seuz-s2cv
Preliminary 2024-2025 U.S. COVID-19 Burden Estimates
Description: This dataset represents preliminary estimates of cumulative U.S. COVID-19 disease burden for the 2024-2025 period, including illnesses, outpatient visits, hospitalizations, and deaths. The weekly COVID-19-associated burden estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. The data come from the Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET), a surveillance platform that captures data from hospitals that serve about 10% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of COVID-19 -associated burden that have occurred since October 1, 2024. Note: Data are preliminary and subject to change as more data become available. Rates for recent COVID-19-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly. References
Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369
Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026
Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
Reed C, Kim IK, Singleton JA, et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm
Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413
Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
COVID-NET | COVID-19 | CDC
https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html
Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.
Schema: dwv_pub_health_surv
Table Name: prelim_2024_2025_us_covid_burden_estima__ahrf_yqdt
Dataset ID: ahrf-yqdt
Category: Public Health Surveillance
Tags: coronavirus, covid-19, deaths, disease burden, hospitalizations, illnesses, outpatient visits
Source Data: https://data.cdc.gov/d/ahrf-yqdt
Preliminary Estimates of Cumulative COVID-19-associated Hospitalizations by Week for 2024-2025
Description: This dataset represents preliminary weekly estimates of cumulative U.S. COVID-19-associated hospitalizations for the 2024-2025 period. The weekly cumulatve COVID-19 –associated hospitalization estimates are preliminary, and use reported weekly hospitalizations among laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. The data are updated week-by-week as new COVID-19 hospitalizations are reported to CDC from the COVID-NET system and include both new admissions that occurred during the reporting week, as well as those admitted in previous weeks that may not have been included in earlier reporting. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of COVID-19 -associated hospitalizations that have occurred since October 1, 2024. For details, please refer to the publication [7]. Note: Data are preliminary and subject to change as more data become available. Rates for recent COVID-19-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly. References
Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369
Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026
Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
Reed C, Kim IK, Singleton JA, et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm
Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413
Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
COVID-NET | COVID-19 | CDC
https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html
Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.
Schema: dwv_pub_health_surv
Table Name: prelim_covid19_hosp_est_by_week_2024_20__xnjn_rdmd
Dataset ID: xnjn-rdmd
Category: Public Health Surveillance
Tags: coronavirus, covid-19, hospitalizations
Source Data: https://data.cdc.gov/d/xnjn-rdmd
Preliminary Estimates of Cumulative RSV-associated Hospitalizations by Week for 2024-2025 season
Description: This dataset represents preliminary weekly estimates of cumulative U.S. RSV-associated hospitalizations for the 2024-2025 season. Estimates are preliminary, and use reported weekly hospitalizations among laboratory-confirmed respiratory syncytial virus (RSV) infections. The data are updated week-by-week as new RSV-associated hospitalizations are reported to CDC from the RSV-NET surveillance system and include both new admissions that occurred during the reporting week, as well as those admitted in previous weeks that may not have been included in earlier reporting. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of RSV-associated hospitalizations that have occurred since October 1, 2024. For details, please refer to the publication [7]. Note: Data are preliminary and subject to change as more data become available. Rates for recent RSV-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly. Note: Preliminary burden estimates are not inclusive of data from all RSV-NET sites. Due to model limitations, sites with small sample sizes can impact estimates in unpredictable ways and are excluded for the benefit of model stability. CDC is working to address model limitations and include data from all sites in final burden estimates. References
Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369
Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026
Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
Reed C, Kim IK, Singleton JA, et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm
Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413
Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
COVID-NET | COVID-19 | CDC
https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html
Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.
Schema: dwv_pub_health_surv
Table Name: prelim_rsv_hospitalizations_weekly_2024__hmye_mqgq
Dataset ID: hmye-mqgq
Category: Public Health Surveillance
Tags: hospitalizations, respiratory syncytial virus, rsv
Source Data: https://data.cdc.gov/d/hmye-mqgq
Preliminary 2024-2025 U.S. RSV Burden Estimates
Description: This dataset represents preliminary estimates of cumulative U.S. RSV –associated disease burden estimates for the 2024-2025 season, including outpatient visits, hospitalizations, and deaths. Real-time estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed respiratory syncytial virus (RSV) infections. The data come from the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET), a surveillance platform that captures data from hospitals that serve about 8% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of RSV-associated disease burden estimates that have occurred since October 1, 2024. Note: Data are preliminary and subject to change as more data become available. Rates for recent RSV-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly. Note: Preliminary burden estimates are not inclusive of data from all RSV-NET sites. Due to model limitations, sites with small sample sizes can impact estimates in unpredictable ways and are excluded for the benefit of model stability. CDC is working to address model limitations and include data from all sites in final burden estimates. References
Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369
Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026
Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
Reed C, Kim IK, Singleton JA, et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm
Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413
Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
COVID-NET | COVID-19 | CDC
https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html
Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.
Schema: dwv_pub_health_surv
Table Name: prelim_us_rsv_burden_estimates_2024_202__sumd_iwm8
Dataset ID: sumd-iwm8
Category: Public Health Surveillance
Tags: deaths, disease burden, hospitalizations, outpatient visits, respiratory syncytial virus, rsv
Source Data: https://data.cdc.gov/d/sumd-iwm8
Prolonged Unplanned School Closures: USA, 2011-2019
Description: Prolonged unplanned public K-12 school district and individual school closures in the United States from August 1, 2011 – June 30, 2019. Prolonged unplanned school closure is defined as a school closure lasting ≥5 school days, excluding any scheduled days off.
Schema: dwv_pub_health_surv
Table Name: prolonged_unplanned_school_closures_usa__5iuf_feyd
Dataset ID: 5iuf-feyd
Category: Public Health Surveillance
Tags: community mitigation, emergency preparedness, pandemic preparedness, school closure
Rates of COVID-19 Cases or Deaths by Age Group and Updated (Bivalent) Booster Status
Description: Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Updated (Bivalent) Booster Status. Click 'More' for important dataset description and footnotes Webpage: https://covid.cdc.gov/covid-data-tracker/#rates-by-vaccine-status Dataset and data visualization details: These data were posted and archived on May 30, 2023 and reflect cases among persons with a positive specimen collection date through April 22, 2023, and deaths among persons with a positive specimen collection date through April 1, 2023. These data will no longer be updated after May 2023. Vaccination status: A person vaccinated with at least a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. A person vaccinated with a primary series and a monovalent booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably receiving a primary series of an FDA-authorized or approved vaccine and at least one additional dose of any monovalent FDA-authorized or approved COVID-19 vaccine on or after August 13, 2021. (Note: this definition does not distinguish between vaccine recipients who are immunocompromised and are receiving an additional dose versus those who are not immunocompromised and receiving a booster dose.) A person vaccinated with a primary series and an updated (bivalent) booster dose had SARS-CoV-2 RNA or antigen detected in a respiratory specimen collected ≥14 days after verifiably receiving a primary series of an FDA-authorized or approved vaccine and an additional dose of any bivalent FDA-authorized or approved vaccine COVID-19 vaccine on or after September 1, 2022. (Note: Doses with bivalent doses reported as first or second doses are classified as vaccinated with a bivalent booster dose.) People with primary series or a monovalent booster dose were combined in the “vaccinated without an updated booster” category. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Per the interim guidance of the Council of State and Territorial Epidemiologists (CSTE), this should include persons whose death certificate lists COVID-19 disease or SARS-CoV-2 as the underlying cause of death or as a significant condition contributing to death. Rates of COVID-19 deaths by vaccination status are primarily reported based on when the patient was tested for COVID-19. In select jurisdictions, deaths are included that are not laboratory confirmed and are reported based on alternative dates (i.e., onset date for most; or date of death or report date, where onset date is unavailable). Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 24 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Colorado, District of Columbia, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (NY), North Carolina, Rhode Island, Tennessee, Texas, Utah, and West Virginia; 23 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 48% of the total U.S. population and all ten of the Health and Human Services Regions. This list will be
Schema: dwv_pub_health_surv
Table Name: rates_covid19_cases_deaths_age_booster___54ys_qyzm
Dataset ID: 54ys-qyzm
Category: Public Health Surveillance
Tags: covid-19 bivalent boosters, covid-19 booster doses, covid-19 breakthrough, covid-19 cases, covid-19 deaths
Rates of COVID-19 Cases or Deaths by Age Group and Vaccination Status
Description: Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022. Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases
Schema: dwv_pub_health_surv
Table Name: rates_covid19_cases_deaths_age_vaccinat__3rge_nu2a
Dataset ID: 3rge-nu2a
Category: Public Health Surveillance
Rates of COVID-19 Cases or Deaths by Age Group and Vaccination Status and Booster Dose
Description: Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022. Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases
Schema: dwv_pub_health_surv
Table Name: rates_covid19_cases_deaths_age_vacc_boo__d6p8_wqjm
Dataset ID: d6p8-wqjm
Category: Public Health Surveillance
RESP-LENS
Description: During the 2022-23 and 2023-24 respiratory illness seasons, the Respiratory Virus Laboratory Emergency Department Network Surveillance (RESP-LENS) system gathered electronic health record (EHR) data from nearly 100 hospitals associated with 24 participating sites located in 20 states and the District of Columbia. There was at least one site in each of the 10 Health and Human Services (HHS) regions. RESP-LENS collected reports of emergency department (ED) visits for acute respiratory illness (ARI) and corresponding viral testing results for SARS-CoV-2 (the virus that causes COVID-19), influenza (flu), and respiratory syncytial virus (RSV). Sites reported data for an average of approximately 77,000 ED visits weekly, of which 11,000 were associated with ARI and 4,000 of those were in children younger than 18 years. Between May 2023 and August 2024, these data were posted to CDC's RESP-LENS public dashboard, showing number tested, number positive, and percent positive for each of the three viruses mentioned, by virus, region, and age group. Due to budget constraints, RESP-LENS was not funded beyond the end of the 2024 fiscal year. RESP-LENS served as a valuable tool for public health and health care professionals and allowed users to visualize and understand trends in virus circulation, estimate disease burden, respond to outbreaks, and inform decisions and strategies for protecting public health.
Schema: dwv_pub_health_surv
Table Name: resp_lens_xplanation_he_dataset_name_co__ch5i_63ve
Dataset ID: ch5i-63ve
Category: Public Health Surveillance
Tags: coronavirus, covid-19, emergency department, flu, influenza, ncird, ncird-corvd, ncird-id, respiratory disease, respiratory syncytial virus, rsv, sars-cov-2
Source Data: https://data.cdc.gov/d/ch5i-63ve
Monthly Rates of Laboratory-Confirmed RSV Hospitalizations from the RSV-NET Surveillance System
Description: The Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET) is a network that conducts active, population-based surveillance for laboratory-confirmed RSV-associated hospitalizations in children younger than 18 years of age and adults. RSV-NET, along with the Coronavirus Disease 2019 (COVID-19) Hospitalization Surveillance Network (COVID-NET) an the Influenza Hospitalization Surveillance network (FluSuv-NET), comprise the Respiratory Virus Hospitalization Surveillance Network (RESP-NET). The RESP-NET platforms have overlapping surveillance areas and use similar methods to collect data. Because the surveillance areas and age groups included in surveillance have changed over time, trends should be interpreted with caution. RSV-NET is CDC’s source for important data on rates of hospitalizations associated with RSV. Hospitalization rates show how many people in the surveillance area are hospitalized with RSV, compared to the total number of people residing in that area. Data are preliminary and subject to change as more data become available. Data will be updated weekly.
Schema: dwv_pub_health_surv
Table Name: rsvmonthlyrates_rsvnet_xplanation_onthl__pbq2_7wr2
Dataset ID: pbq2-7wr2
Category: Public Health Surveillance
Tags: age-adjusted rates, age-adjusted rates by race and ethnicity, hospitalization rate, hospitalizations, rate, rates by age group, rates by race and ethnicity, respiratory disease, respiratory illness, respiratory syncytial virus, respiratory virus response, rsv, rsvnet, rsv-net, surveillance
Source Data: https://data.cdc.gov/d/pbq2-7wr2
Rates of Laboratory-Confirmed RSV, COVID-19, and Flu Hospitalizations from the RESP-NET Surveillance Systems
Description: The Respiratory Virus Hospitalization Surveillance Network (RESP-NET) is a network that conducts, active, population-based surveillance for laboratory confirmed hospitalizations associated with Influenza, COVID-19, and RSV. The RESP-NET platforms have overlapping surveillance areas and use similar methods to collect data. Hospitalization rates show how many people in the surveillance area are hospitalized with influenza, COVID-19, and RSV compared to the total number of people residing in that area. Data will be updated weekly. Data are preliminary and subject to change as more data become available.
Schema: dwv_pub_health_surv
Table Name: rsv_covid19_flu_hospitalizations_respne__kvib_3txy
Dataset ID: kvib-3txy
Category: Public Health Surveillance
Tags: covid, covid19, covid-19, covidnet, covid-net, flu, flunet, flu-net, flusurvnet, flusurv-net, hospitalization, hospitalization rate, hospitalizations, influenza, rate, rates by age group, rates by race and ethnicity, respiratory illness, respiratory syncytial virus, respiratory virus response, respnet, resp-net, rsv, surveillance
Source Data: https://data.cdc.gov/d/kvib-3txy
Weekly Rates of Laboratory-Confirmed RSV Hospitalizations from the RSV-NET Surveillance System
Description: The Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET) is a network that conducts active, population-based surveillance for laboratory-confirmed RSV-associated hospitalizations in children younger than 18 years of age and adults. RSV-NET, along with the Coronavirus Disease 2019 (COVID-19) Hospitalization Surveillance Network (COVID-NET) an the Influenza Hospitalization Surveillance network (FluSuv-NET), comprise the Respiratory Virus Hospitalization Surveillance Network (RESP-NET). The RESP-NET platforms have overlapping surveillance areas and use similar methods to collect data. Because the surveillance areas and age groups included in surveillance have changed over time, trends should be interpreted with caution. RSV-NET is CDC’s source for important data on rates of hospitalizations associated with RSV. Hospitalization rates show how many people in the surveillance area are hospitalized with RSV, compared to the total number of people residing in that area. Data are preliminary and subject to change as more data become available. Data will be updated weekly.
Schema: dwv_pub_health_surv
Table Name: rsv_weekly_hospitalization_rates_xplana__29hc_w46k
Dataset ID: 29hc-w46k
Category: Public Health Surveillance
Tags: age-adjusted rates, surveillance, rsv-net, rsvnet, rsv, respiratory virus response, respiratory syncytial virus, respiratory illness, respiratory disease, rates by race and ethnicity, rate, hospitalizations, hospitalization rate, hospitalization, age-adjusted rates by race and ethnicity
Source Data: https://data.cdc.gov/d/29hc-w46k
Respiratory Virus Response (RVR) United States Hospitalization Metrics by Jurisdiction, Timeseries – ARCHIVED
Description: Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 and influenza hospital admissions, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). This dataset represents hospitalization data and metrics aggregated to country, HHS region, and state/territory. Hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to hospital admissions, and inpatient and ICU bed capacity occupancy. Data fields for new admissions of pediatric patients with confirmed COVID-19 for ages 0-4 years, 5-11 years, and 12-17 years were not required for reporting until February 2022; therefore, data for the following fields in this dataset begin on March 1, 2022 to account for delays in initial reporting of these fields: adm_00_04_covid_confirmed avg_adm_00_04_covid_confirmed avg_adm_00_04_covid_confirmed_per_100k adm_05_11_covid_confirmed avg_adm_05_11_covid_confirmed avg_adm_05_11_covid_confirmed_per_100k adm_12_17_covid_confirmed avg_adm_12_17_covid_confirmed avg_adm_12_17_covid_confirmed_per_100k Updated weekly each Friday at noon, ET.
Schema: dwv_pub_health_surv
Table Name: rvirus_response_us_hospitalization_metr__9t9r_e5a3
Dataset ID: 9t9r-e5a3
Category: Public Health Surveillance
Tags: admissions, coronavirus, covid-19, hospital capacity, hospitalizations
Total COVID-19 Deaths since January 1, 2020 by Age Group, Race/Ethnicity, and Sex
Description: Count and percent of total COVID-19 deaths since January 1, 2020, by age group, race/ethnicity, and sex
Schema: dwv_pub_health_surv
Table Name: total_covid19_deaths_by_age_race_ethnic__kmxt_xb3i
Dataset ID: kmxt-xb3i
Category: Public Health Surveillance
Tags: demographics, ncird-corvd, coronavirus, covid-19, deaths, mortality
Source Data: https://data.cdc.gov/d/kmxt-xb3i
United States COVID-19 Community Levels by County
Description: Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated. This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties. The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge. Using these data, the COVID-19 community level was classified as low, medium, or high. COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals. For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates. Archived Data Notes: This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022. March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released. March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate. March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset. March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases. March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average). March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior. April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error. April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials t
Schema: dwv_pub_health_surv
Table Name: us_covid19_community_levels_county__3nnm_4jni
Dataset ID: 3nnm-4jni
Category: Public Health Surveillance
Tags: cases, community levels, coronavirus, county, covid-19, hospital, ncird-corvd, united states
Source Data: https://data.cdc.gov/d/3nnm-4jni
United States COVID-19 County Level Data Sources - ARCHIVED
Description: The Public Health Emergency (PHE) declaration for COVID-19 expired on May 11, 2023. As a result, the Aggregate Case and Death Surveillance System will be discontinued. Although these data will continue to be publicly available, this dataset will no longer be updated. On October 20, 2022, CDC began retrieving aggregate case and death data from jurisdictional and state partners weekly instead of daily. This dataset includes the URLs that were used by the aggregate county data collection process that compiled aggregate case and death counts by county. Within this file, each of the states (plus select jurisdictions and territories) are listed along with the county web sources which were used for pulling these numbers. Some states had a single statewide source for collecting the county data, while other states and local health jurisdictions may have had standalone sources for individual counties. In the cases where both local and state web sources were listed, a composite approach was taken so that the maximum value reported for a location from either source was used. The initial raw data were sourced from these links and ingested into the CDC aggregate county dataset before being published on the COVID Data Tracker.
Schema: dwv_pub_health_surv
Table Name: us_covid19_county_data_sources_archived__7pvw_pdbr
Dataset ID: 7pvw-pdbr
Category: Public Health Surveillance
Tags: aggregate county data, cases, coronavirus, covid-19, ncird-corvd
Source Data: https://data.cdc.gov/d/7pvw-pdbr
United States COVID-19 County Level of Community Transmission as Originally Posted - ARCHIVED
Description: On October 20, 2022, CDC began retrieving aggregate case and death data from jurisdictional and state partners weekly instead of daily. This dataset contains archived community transmission and related data elements by county as originally displayed on the COVID Data Tracker. Although these data will continue to be publicly available, this dataset has not been updated since October 20, 2022. An archived dataset containing weekly community transmission data by county as originally posted can also be found here: Weekly COVID-19 County Level of Community Transmission as Originally Posted | Data | Centers for Disease Control and Prevention (cdc.gov). Related data CDC has been providing the public with two versions of COVID-19 county-level community transmission level data: this dataset with the daily values as originally posted on the COVID Data Tracker, and an historical dataset with daily data as well as the updates and corrections from state and local health departments. Similar to this dataset, the original historical dataset is archived on 10/20/2022. It will continue to be publicly available but will no longer be updated. A new dataset containing historical community transmission data by county is now published weekly and can be found at: Weekly COVID-19 County Level of Community Transmission Historical Changes | Data | Centers for Disease Control and Prevention (cdc.gov). This public use dataset has 7 data elements reflecting community transmission levels for all available counties and jurisdictions. It contains reported daily transmission levels at the county level with the same values used to display transmission maps on the COVID Data Tracker. Each day, the dataset is appended to contain the most recent day's data. Transmission level is set to low, moderate, substantial, or high using the calculation rules below. Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making. CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2 Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have a transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00). Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests conducted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have a transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00). If
Schema: dwv_pub_health_surv
Table Name: us_covid19_county_transmission_archive__8396_v7yb
Dataset ID: 8396-v7yb
Category: Public Health Surveillance
Tags: case, community transmission, coronavirus, county, covid-19, laboratory, ncird-corvd
Source Data: https://data.cdc.gov/d/8396-v7yb
United States COVID-19 County Level of Community Transmission Historical Changes - ARCHIVED
Description: On October 20, 2022, CDC began retrieving aggregate case and death data from jurisdictional and state partners weekly instead of daily. This dataset contains archived historical community transmission and related data elements by county. Although these data will continue to be publicly available, this dataset has not been updated since October 20, 2022. An archived dataset containing weekly historical community transmission data by county can also be found here: Weekly COVID-19 County Level of Community Transmission Historical Changes | Data | Centers for Disease Control and Prevention (cdc.gov). Related data CDC has been providing the public with two versions of COVID-19 county-level community transmission level data: this historical dataset with the daily county-level transmission data from January 22, 2020, and a dataset with the daily values as originally posted on the COVID Data Tracker. Similar to this dataset, the original dataset with daily data as posted is archived on 10/20/2022. It will continue to be publicly available but will no longer be updated. A new dataset containing community transmission data by county as originally posted is now published weekly and can be found at: Weekly COVID-19 County Level of Community Transmission as Originally Posted | Data | Centers for Disease Control and Prevention (cdc.gov). This public use dataset has 7 data elements reflecting historical data for community transmission levels for all available counties and jurisdictions. It contains historical data for the county level of community transmission and includes updated data submitted by states and jurisdictions. Each day, the dataset was updated to include the most recent days’ data and incorporate any historical changes made by jurisdictions. This dataset includes data since January 22, 2020. Transmission level is set to low, moderate, substantial, or high using the calculation rules below. Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making. CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2 Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00). Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests resulted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substa
Schema: dwv_pub_health_surv
Table Name: us_covid19_county_transmission_changes___nra9_vzzn
Dataset ID: nra9-vzzn
Category: Public Health Surveillance
Tags: cases, community transmission, coronavirus, county, covid-19, laboratory, ncird-corvd
Source Data: https://data.cdc.gov/d/nra9-vzzn
United States COVID-19 Hospitalization Metrics by Jurisdiction, Timeseries – ARCHIVED
Description: Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. The related CDC COVID Data Tracker site was revised or retired on May 10, 2023. This dataset represents daily COVID-19 hospitalization data and metrics aggregated to national, state/territory, and regional levels. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy. Reporting information:
As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf
Metric details:
Time Period: timeseries data will update weekly on Mondays as soon as they are reviewed and verified, usually before 8 pm ET. Updates will occur the following day when reporting coincides with a federal holiday. Note: Weekly updates might be delayed due to delays in reporting. All data are provisional. Because these provisional counts are subject to change, including updates to data reported previously, adjustments can occur. Data may be updated since original publication due to delays in reporting (to account for data received after a given Thursday publication) or data quality corrections.
New COVID-19 Hospital Admissions (count): Number of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
New COVID-19 Hospital Admissions (7-Day Average): 7-day average of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
Cumulative COVID-19 Hospital Admissions: Cumulative total number of admissions of patients with laborat
Schema: dwv_pub_health_surv
Table Name: us_covid19_hospitalization_metrics_juri__39z2_9zu6
Dataset ID: 39z2-9zu6
Category: Public Health Surveillance
Tags: admissions, coronavirus, covid-19, hospital capacity, hospitalizations, hospital occupancy, icu beds, inpatient beds, ncird-corvd
Source Data: https://data.cdc.gov/d/39z2-9zu6
U.S. COVID-19 MakeMyTestCount Self-Test Data
Description: This dataset includes COVID-19 self-test result data voluntarily reported by users of tests through the MakeMyTestCount website (makemytestcount.org). All fields are self-reported by the user with the exception of fields derived from the self-reported zip code. This dataset will be updated monthly. If there are any questions, please direct them to the data steward, Jasmine Chaitram . This dataset includes the following self-reported data: - Date (by week)– date of test shown by week starting date - Age group (years) – age of individual taking the test, categorized into the following: 2-4, 5-11, 12-15, 16-17, 18-29, 30-39, 40-49, 50-64, 65-74, 75+ - Race – race of individual taking the test: American Indian or Alaska Native, Asian, Black, Native Hawaiian or Other Pacific Islander, White, Multiple or Other, missing - Ethnicity – ethnicity of individual taking the test: Hispanic, Non-Hispanic, missing - Sex – sex of individual taking the test: male, female, missing - Test result – positive, negative, inconclusive The dataset also includes the following columns to support analyses. These columns are based on the self-reported zip code: - State abbreviation - State name - State FIPS code - FEMA region Please note that there are limitations with these data, including: 1. Data are not comprehensive of all self-tests performed. Data represent results voluntarily reported by an individual via the MakeMyTestCount website. These data do not include self-test results that were reported to state and local health departments if they were not also reported through the MakeMyTestCount website. The true denominator (known number of tests completed in the US) cannot be ascertained and reflects a small fraction of the number of self-tests used. 2. Data are not verified. The quality of specimen, appropriate execution of self-test, result produced, and person tested are unverified; therefore, reported interpretation of results cannot be confirmed. All results and accompanying demographic information are also self-reported and cannot be verified. 3. Data reports are not complete. Individual submissions vary widely in terms of the data elements collected. Not all data elements are required (only date, age, and zip code), and some results are missing demographic information. 4. Data are not representative. Based on the limited number of self-reported test results, this dataset is not representative of the use of self-testing by demographic, nor is the dataset inclusive of all self-testing completed within each jurisdiction. This dataset represents a small proportion of overall COVID-19 testing conducted and reported volumes are much lower than testing conducted in point of care and laboratory settings. 5. Data represent individual test results, not persons tested. Data in this dataset are not linkable and do not allow for analyses around serial testing. Data also cannot be disaggregated to identify multiple reports by the same individual. All analyses should be completed with these limitations in mind. For more information about the challenges and opportunities around self-test data, please refer to the following article: Ritchey MD, Rosenblum HG, Del Guercio K, et al. COVID-19 Self-Test Data: Challenges and Opportunities — United States, October 31, 2021–June 11, 2022. MMWR Morb Mortal Wkly Rep 2022;71:1005–1010. DOI: http://dx.doi.org/10.15585/mmwr.mm7132a1
Schema: dwv_pub_health_surv
Table Name: us_covid19_makemytestcount_selftest_dat__i2a4_xk9k
Dataset ID: i2a4-xk9k
Category: Public Health Surveillance
Tags: coronavirus, covid-19, mmtc, otc, self-tests
U.S. COVID-19 Self-Test Data
Description: This dataset includes COVID-19 self-test result data voluntarily reported by users of tests through manufacturer websites and mobile companion applications. At this time, the dataset does not include data reported through the MakeMyTestCount website. All fields are self-reported by the user voluntarily reporting the test result. This dataset will be updated monthly. Please note that there are limitations with these data, including: 1. Data are not comprehensive of all self-tests performed. Data represent results voluntarily reported by an individual via manufacturer-provided website or companion mobile applications. Not all self-test manufacturers are currently capturing and sending data to CDC. Similarly, these data do not include self-test results that were reported to state and local health departments if they were not also reported through the manufacturer-provided website or companion mobile applications. The true denominator (number of tests completed) cannot be ascertained, but based on manufacturer production numbers, this dataset reflects a small fraction of the number of self-tests used. 2. Data are not verified. The quality of specimen, appropriate execution of self-test, result produced, and person tested are unverified; therefore, reported interpretation of results cannot be confirmed. All results and accompanying demographic information are also self-reported and cannot be verified. 3. Data reports are not complete. Manufacturer-provided websites and companion mobile applications vary widely in terms of the data elements collected. Not all data elements are required, and many results are missing demographic information. 4. Data are not representative. Based on the limited number of self-reported test results, this dataset is not representative of the use of self-testing by demographic, nor is the dataset inclusive of all self-testing completed within each jurisdiction. This dataset represents a small proportion of overall COVID-19 testing conducted in each jurisdiction and reported volumes are much lower than testing conducted in point of care and laboratory settings. 5. Data represent individual test results, not persons tested. Data in this dataset are not linkable and do not allow for analyses around serial testing. Data also cannot be disaggregated to identify multiple reports by the same individual. All analyses should be completed with these limitations in mind. For more information about the challenges and opportunities around self-test data, please refer to the following article: Ritchey MD, Rosenblum HG, Del Guercio K, et al. COVID-19 Self-Test Data: Challenges and Opportunities — United States, October 31, 2021–June 11, 2022. MMWR Morb Mortal Wkly Rep 2022;71:1005–1010. DOI: http://dx.doi.org/10.15585/mmwr.mm7132a1
Schema: dwv_pub_health_surv
Table Name: us_covid19_selftest_data__275g_9x8h
Dataset ID: 275g-9x8h
Category: Public Health Surveillance
Tags: coronavirus, covid-19, otc, self-tests
Weekly COVID-19 cases among persons ≥5 years old among unvaccinated and vaccinated with a BNT162b2 (Pfizer-BioNTech) primary series by age group — 22 U.S. jurisdictions, January 16 to May 28, 2022
Description: Reported numbers of SARS-CoV-2 infections by age group (5–11, 12–17, 18–49, 50–64, ≥65 years of age) from 22 U.S. jurisdictions (AR, AZ, CA, CO, CT, DC, FL, GA, IN, KS, MI, MA, MN, NC, NE, NJ, NM, NYC, PHL, TN, UT, WI ); ~53% of the U.S. population) with routine linkages between COVID-19 case surveillance and immunization information system (IIS) data reported to CDC during January 16, 2022 – May 28, 2022. Vaccine administration (coverage) data reported to CDC were aggregated by U.S. reporting jurisdiction, MMWR week of vaccination (≥14 days after completing the primary vaccine series), FDA-approved vaccine products, and age group (5–11, 12–17, 18–49, 50–64, ≥65 years). Vaccination status: A person vaccinated with at least a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing BNT162b2 (Pfizer-BioNTech) primary series. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. To estimate the number of unvaccinated persons in each MMWR week, the 2019 U.S. Census population estimates by jurisdiction and age group were used (except for California, where State Department of Finance 2021 population projections were determined to be more accurate). The number of unvaccinated persons each MMWR week was estimated by subtracting the cumulative number of vaccinated (all products) and partially vaccinated persons (all products) from the respective population totals for each jurisdiction and age group. Continuity correction: A continuity correction has been applied to the denominators by capping the percent population coverage at 95%. To do this, we assumed that at least 5% of each age group would always be unvaccinated in each jurisdiction. Adding this correction ensures that there is always a reasonable denominator for the unvaccinated population that would prevent rates from growing unrealistically large due to potential overestimates of vaccination coverage.
Schema: dwv_pub_health_surv
Table Name: weekly_covid19_cases_bnt162b2_vacc_stat__v2zw_2d2v
Dataset ID: v2zw-2d2v
Category: Public Health Surveillance
Tags: covid-19 breakthrough, covid-19 cases, covid-19 vaccination
Weekly COVID-19 County Level of Community Transmission as Originally Posted - ARCHIVED
Description: Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated. Weekly COVID-19 Community Levels (CCLs) have been replaced with levels of COVID-19 hospital admission rates (low, medium, or high) which demonstrate >99% concordance by county during February 2022–March 2023. For more information on the latest COVID-19 status levels in your area and hospital admission rates, visit United States COVID-19 Hospitalizations, Deaths, and Emergency Visits by Geographic Area. This archived public use dataset contains historical case and percent positivity data updated weekly for all available counties and jurisdictions. Each week, the dataset was refreshed to capture any historical updates. Please note, percent positivity data may be incomplete for the most recent time period. This archived public use dataset contains weekly community transmission levels data for all available counties and jurisdictions since October 20, 2022. The dataset was appended to contain the most recent week's data as originally posted on COVID Data Tracker. Historical corrections are not made to these data if new case or testing information become available. A separate archived file is made available here (: Weekly COVID-19 County Level of Community Transmission Historical Changes) if historically updated data are desired. Related data CDC provides the public with two active versions of COVID-19 county-level community transmission level data: this dataset with the levels as originally posted (Weekly Originally Posted dataset), updated weekly with the most recent week’s data since October 20, 2022, and a historical dataset with the county-level transmission data from January 22, 2020 (Weekly Historical Changes dataset). Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making. CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2 Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have a transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00). Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests conducted
Schema: dwv_pub_health_surv
Table Name: weekly_covid19_county_transmission_arch__dt66_w6m6
Dataset ID: dt66-w6m6
Category: Public Health Surveillance
Tags: case, community transmission, coronavirus, county, covid-19, laboratory, ncird-corvd
Source Data: https://data.cdc.gov/d/dt66-w6m6
Weekly COVID-19 County Level of Community Transmission Historical Changes - ARCHIVED
Description: Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. This dataset will receive a final update on June 1, 2023, to reconcile historical data through May 10, 2023, and will remain publicly available. This archived public use dataset contains historical case and percent positivity data updated weekly for all available counties and jurisdictions. Each week, the dataset was refreshed to capture any historical updates. Please note, percent positivity data may be incomplete for the most recent time period. Related data CDC provides the public with two active versions of COVID-19 county-level community transmission level data: this dataset with historical case and percent positivity data for each county from January 22, 2020 (Weekly Historical Changes dataset) and a dataset with the levels as originally posted (Weekly Originally Posted dataset) since October 20, 2022. Please navigate to the Weekly Originally Posted dataset for the Community Transmission Levels published weekly on Thursdays. Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making. CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2 Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00). Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests resulted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00). The data in this dataset are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers. This dataset is created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access. Archived data CDC has archived two prior versions of these datasets. Both versions contain the same 7 data elements reflecting community transmission levels for all available counties and jurisdictions; however, the datasets updated daily. The archived datasets can be found here: <a href="https://data.cdc.gov/Public-Health-Surveillance/United-States-COVID-19-County-Level-of-Community-T
Schema: dwv_pub_health_surv
Table Name: weekly_covid19_county_transmission_chan__jgk8_6dpn
Dataset ID: jgk8-6dpn
Category: Public Health Surveillance
Tags: cases, community transmission, coronavirus, county, covid-19, laboratory, ncird-corvd
Source Data: https://data.cdc.gov/d/jgk8-6dpn
Weekly Hospital Respiratory Data (HRD) Metrics by Jurisdiction, National Healthcare Safety Network (NHSN) (Preliminary)
Description: This dataset represents preliminary weekly hospital respiratory data and metrics aggregated to national and state/territory levels reported to CDC’s National Health Safety Network (NHSN) beginning August 2020. This dataset updates weekly on Wednesdays with preliminary data reported to NHSN for the previous reporting week (Sunday – Saturday). Data for reporting dates through April 30, 2024 represent data reported during a previous mandated reporting period as specified by the HHS Secretary. Data for reporting dates May 1, 2024 – October 31, 2024 represent voluntarily reported data in the absence of a mandate. Data for reporting dates beginning November 1, 2024 represent data reported during a current mandated reporting period. All data and metrics capturing information on respiratory syncytial virus (RSV) were voluntarily reported until November 1, 2024. All data included in this dataset represent aggregated counts, and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and new hospital admissions with corresponding metrics indicating reporting coverage for a given reporting week. NHSN monitors national and local trends in healthcare system stress and capacity for all acute care and critical access hospitals in the United States. For more information on the reporting mandate per the Centers for Medicare and Medicaid Services (CMS) requirements, visit: Updates to the Condition of Participation (CoP) Requirements for Hospitals and Critical Access Hospitals (CAHs) To Report Acute Respiratory Illnesses. For more information regarding NHSN’s collection of these data, including full reporting guidance, visit: NHSN Hospital Respiratory Data. For data that is considered final for a given reporting week (Sunday – Saturday), and reflects that which is used in NHSN HRD dashboards for publication each Friday, visit: https://data.cdc.gov/Public-Health-Surveillance/Weekly-Hospital-Respiratory-Data-HRD-Metrics-by-Ju/ua7e-t2fy/about_data. CDC coordinates weekly forecasts of hospitalization admissions based on this data set. More information about flu forecasting can be found at About Flu Forecasting | FluSight | CDC, and information about COVID-19 forecasting and other modeling analyses for the Respiratory Virus Season are available at CFA's Insights for Respiratory Virus Season | CFA | CDC. Source: CDC National Healthcare Safety Network (NHSN).
Data source description (updated November 15, 2024): As of October 9, 2024, Hospital Respiratory Data (HRD; formerly Respiratory Pathogen, Hospital Capacity, and Supply data or 'COVID-19 hospital data') are reported to HHS through CDC's National Healthcare Safety Network (NHSN) based on updated requirements from the Centers for Medicare and Medicaid Services (CMS). These data were voluntarily reported to NHSN May 1, 2024 until November 1, 2024, at which time CMS began requiring acute care and critical access hospitals to electronically report information via NHSN about COVID-19, influenza, and RSV, hospital bed census and capacity. Hospital bed capacity and occupancy data for all patients and for patients with COVID-19 or influenza for collection dates prior to May 1, 2024, represent data reported during a previously mandated reporting
Schema: dwv_pub_health_surv
Table Name: weekly_hospital_respiratory_data_hrd_nh__mpgq_jmmr
Dataset ID: mpgq-jmmr
Category: Public Health Surveillance
Tags: covid19, hospital capacity, hospitalizations, hospital occupancy, icu beds, influenza, respiratory, respiratory syncytial virus, rsv, inpatient beds, admissions, coronavirus
Source Data: https://data.cdc.gov/d/mpgq-jmmr
Weekly Hospital Respiratory Data (HRD) Metrics by Jurisdiction, National Healthcare Safety Network (NHSN) (Historical)
Description: This dataset represents weekly hospital respiratory data and metrics aggregated to national and state/territory levels reported to CDC’s National Health Safety Network (NHSN) beginning November 2024. Data and metrics included in this dataset are NOT updated or adjusted week-over-week after initial publication, and therefore represent data received at the time of publication for a given reporting week. All data included in this dataset represent aggregated counts, and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and new hospital admissions with corresponding metrics indicating reporting coverage for a given reporting week. NHSN monitors national and local trends in healthcare system stress and capacity for all acute care and critical access hospitals in the United States. For more information on the reporting mandate per the Centers for Medicare and Medicaid Services (CMS) requirements, visit: Updates to the Condition of Participation (CoP) Requirements for Hospitals and Critical Access Hospitals (CAHs) To Report Acute Respiratory Illnesses. For more information regarding NHSN’s collection of these data, including full reporting guidance, visit: NHSN Hospital Respiratory Data. Source: CDC National Healthcare Safety Network (NHSN).
Data source description (updated November 15, 2024): As of October 9, 2024, Hospital Respiratory Data (HRD; formerly Respiratory Pathogen, Hospital Capacity, and Supply data or 'COVID-19 hospital data') are reported to HHS through CDC's National Healthcare Safety Network (NHSN) based on updated requirements from the Centers for Medicare and Medicaid Services (CMS). These data were voluntarily reported to NHSN May 1, 2024 until November 1, 2024, at which time CMS began requiring acute care and critical access hospitals to electronically report information via NHSN about COVID-19, influenza, and RSV, hospital bed census and capacity. Hospital bed capacity and occupancy data for all patients and for patients with COVID-19 or influenza for collection dates prior to May 1, 2024, represent data reported during a previously mandated reporting period as specified by the HHS Secretary, and data for collection dates May 1, 2024 – October 31, 2024 represent data reported voluntarily to NHSN. All RSV data through October 31, 2024 represent voluntarily reported data; as such, all voluntarily reported data included in this dataset represent reporting hospitals only for a given week and might not be complete or representative of all hospitals during the specified reporting periods.
NHSN monitors national and local trends in healthcare system stress and capacity for all acute care and critical access hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Find more information about reporting to NHSN: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.
Data quality: While CDC reviews reported data for completeness and errors and corrects those found, some reporting errors might still exist within the data. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks. Data reported as of December 1, 2020 are subject to thorough, routine data quality review procedures, including identifying and excluding invalid values from metric calculations and application of error correction methodology; data prior to this date may have anomalies that are not yet resolved. Data prior to August 1, 2020, are unavailab
Schema: dwv_pub_health_surv
Table Name: weekly_hospital_respiratory_data_hrd_nh__rhwp_grxi
Dataset ID: rhwp-grxi
Category: Public Health Surveillance
Tags: admissions, coronavirus, covid-19, hospital capacity, hospitalizations, hospital occupancy, icu beds, influenza, inpatient beds, respiratory, respiratory syncytial virus, rsv
Source Data: https://data.cdc.gov/d/rhwp-grxi
Weekly Hospital Respiratory Data (HRD) Metrics by Jurisdiction, National Healthcare Safety Network (NHSN)
Description: This dataset represents weekly hospital respiratory data and metrics aggregated to national and state/territory levels reported to CDC’s National Health Safety Network (NHSN) beginning August 2020. Data for reporting dates through April 30, 2024 represent data reported during a previous mandated reporting period as specified by the HHS Secretary. Data for reporting dates May 1, 2024 – October 31, 2024 represent voluntarily reported data in the absence of a mandate. Data for reporting dates beginning November 1, 2024 represent data reported during a current mandated reporting period. All data and metrics capturing information on respiratory syncytial virus (RSV) were voluntarily reported until November 1, 2024. All data included in this dataset represent aggregated counts, and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and new hospital admissions with corresponding metrics indicating reporting coverage for a given reporting week. NHSN monitors national and local trends in healthcare system stress and capacity for all acute care and critical access hospitals in the United States. For more information on the reporting mandate per the Centers for Medicare and Medicaid Services (CMS) requirements, visit: Updates to the Condition of Participation (CoP) Requirements for Hospitals and Critical Access Hospitals (CAHs) To Report Acute Respiratory Illnesses. For more information regarding NHSN’s collection of these data, including full reporting guidance, visit: NHSN Hospital Respiratory Data. Source: CDC National Healthcare Safety Network (NHSN).
Data source description (updated November 15, 2024): As of October 9, 2024, Hospital Respiratory Data (HRD; formerly Respiratory Pathogen, Hospital Capacity, and Supply data or 'COVID-19 hospital data') are reported to HHS through CDC's National Healthcare Safety Network (NHSN) based on updated requirements from the Centers for Medicare and Medicaid Services (CMS). These data were voluntarily reported to NHSN May 1, 2024 until November 1, 2024, at which time CMS began requiring acute care and critical access hospitals to electronically report information via NHSN about COVID-19, influenza, and RSV, hospital bed census and capacity. Hospital bed capacity and occupancy data for all patients and for patients with COVID-19 or influenza for collection dates prior to May 1, 2024, represent data reported during a previously mandated reporting period as specified by the HHS Secretary, and data for collection dates May 1, 2024 – October 31, 2024 represent data reported voluntarily to NHSN. All RSV data through October 31, 2024 represent voluntarily reported data; as such, all voluntarily reported data included in this dataset represent reporting hospitals only for a given week and might not be complete or representative of all hospitals during the specified reporting periods.
NHSN monitors national and local trends in healthcare system stress and capacity for all acute care and critical access hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Find more information about reporting to NHSN: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.
Data quality: While CDC reviews reported data for completeness and errors and corrects those found, some reporting errors might still exist within the data. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks. Data reported as of December
Schema: dwv_pub_health_surv
Table Name: weekly_hospital_respiratory_data_jurisd__ua7e_t2fy
Dataset ID: ua7e-t2fy
Category: Public Health Surveillance
Tags: admissions, coronavirus, covid-19, hospital capacity, hospitalizations, hospital occupancy, icu beds, influenza, inpatient beds, respiratory, respiratory syncytial virus, rsv
Source Data: https://data.cdc.gov/d/ua7e-t2fy
Weekly Rates of Laboratory-Confirmed COVID-19 Hospitalizations from the COVID-NET Surveillance System
Description: The Coronavirus Disease 2019 (COVID-19) Hospitalization Surveillance Network (COVID-NET) a network that conducts active, population-based surveillance for laboratory-confirmed COVID-19-associated hospitalizations among children and adults. COVID-NET, along with the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET) and the Influenza Hospitalization Surveillance Network (FluSurv-NET), comprise the Respiratory Virus Hospitalization Surveillance Network (RESP-NET). The RESP-NET platforms have overlapping surveillance areas and use similar methods to collect data. COVID-NET is CDC’s source for important data on rates of hospitalizations associated with COVID-19. Hospitalization rates show how many people in the surveillance area are hospitalized with COVID-19, compared to the total number of people residing in that area. Data are preliminary and subject to change as more data become available. Data will be updated weekly.
Schema: dwv_pub_health_surv
Table Name: weekly_rates_covid19_hospitalizations_c__6jg4_xsqq
Dataset ID: 6jg4-xsqq
Category: Public Health Surveillance
Tags: rate, rates by age group, resp-net, respnet, respiratory virus response, rates by race and ethnicity, respiratory illness, covid-net, covidnet, covid-19, covid, covid19, surveillance, hospitalization rate, hospitalizations
Source Data: https://data.cdc.gov/d/6jg4-xsqq
Weekly United States COVID-19 Hospitalization Metrics by County (Historical) – ARCHIVED
Description: Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). The related CDC COVID Data Tracker site was revised or retired on May 10, 2023. Note: May 3,2024: Due to incomplete or missing hospital data received for the April 21,2024 through April 27, 2024 reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on May 3, 2024. This dataset represents COVID-19 hospitalization data and metrics aggregated to county or county-equivalent, for all counties or county-equivalents (including territories) in the United States as of the initial date of reporting for each weekly metric. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy. Reporting information:
As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf
Calculation of county-level hospital metrics:
County-level hospital data are derived using calculations performed at the Health Service Area (HSA) level. An HSA is defined by CDC’s National Center for Health Statistics as a geographic area containing at least one county which is self-contained with respect to the population’s provision of routine hospital care. Every county in the United States is assigned to an HSA, and each HSA must contain at least one hospital. Therefore, use of HSAs in the calculation of local hospital metrics allows for more accurate characterization of the relationship between health care utilization and health status at the local level.
Data presented at the county-level represent admissions, hosp
Schema: dwv_pub_health_surv
Table Name: weekly_us_covid19_hospitalizations_by_c__82ci_krud
Dataset ID: 82ci-krud
Category: Public Health Surveillance
Tags: admissions, coronavirus, covid-19, hospital capacity, hospitalizations, hospital occupancy, icu beds, inpatient beds, ncird-corvd
Source Data: https://data.cdc.gov/d/82ci-krud
Weekly United States COVID-19 Hospitalization Metrics by County – ARCHIVED
Description: Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). The related CDC COVID Data Tracker site was revised or retired on May 10, 2023. Note: May 3,2024: Due to incomplete or missing hospital data received for the April 21,2024 through April 27, 2024 reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on May 3, 2024. This dataset represents COVID-19 hospitalization data and metrics aggregated to county or county-equivalent, for all counties or county-equivalents (including territories) in the United States. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy. Reporting information:
As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf
Calculation of county-level hospital metrics:
County-level hospital data are derived using calculations performed at the Health Service Area (HSA) level. An HSA is defined by CDC’s National Center for Health Statistics as a geographic area containing at least one county which is self-contained with respect to the population’s provision of routine hospital care. Every county in the United States is assigned to an HSA, and each HSA must contain at least one hospital. Therefore, use of HSAs in the calculation of local hospital metrics allows for more accurate characterization of the relationship between health care utilization and health status at the local level.
Data presented at the county-level represent admissions, hospital inpatient and ICU bed capacity and occupancy among hosp
Schema: dwv_pub_health_surv
Table Name: weekly_us_covid19_hospitalizations_by_c__akn2_qxic
Dataset ID: akn2-qxic
Category: Public Health Surveillance
Tags: admissions, coronavirus, covid-19, hospital capacity, hospitalizations, hospital occupancy, icu beds, inpatient beds, ncird-corvd
Source Data: https://data.cdc.gov/d/akn2-qxic
Weekly United States COVID-19 Hospitalization Metrics by Jurisdiction – ARCHIVED
Description: Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). The related CDC COVID Data Tracker site was revised or retired on May 10, 2023. This dataset represents weekly COVID-19 hospitalization data and metrics aggregated to national, state/territory, and regional levels. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy. Reporting information:
As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf
Metric details:
Time Period: timeseries data will update weekly on Mondays as soon as they are reviewed and verified, usually before 8 pm ET. Updates will occur the following day when reporting coincides with a federal holiday. Note: Weekly updates might be delayed due to delays in reporting. All data are provisional. Because these provisional counts are subject to change, including updates to data reported previously, adjustments can occur. Data may be updated since original publication due to delays in reporting (to account for data received after a given Thursday publication) or data quality corrections.
New COVID-19 Hospital Admissions (count): Number of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
New COVID-19 Hospital Admissions (7-Day Average): 7-day average of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
Cumulative COVID-19 Hospital Admissions: Cumulative total number of admissions of patients with labo
Schema: dwv_pub_health_surv
Table Name: weekly_us_covid19_hospitalizations_by_j__7dk4_g6vg
Dataset ID: 7dk4-g6vg
Category: Public Health Surveillance
Tags: admissions, coronavirus, covid-19, hospital capacity, hospitalizations, hospital occupancy, icu beds, inpatient beds, ncird-corvd
Source Data: https://data.cdc.gov/d/7dk4-g6vg
Weekly United States Hospitalization Metrics by Jurisdiction, During Mandatory Reporting Period from August 1, 2020 to April 30, 2024, and for Data Reported Voluntarily Beginning May 1, 2024, National Healthcare Safety Network (NHSN) - ARCHIVED
Description: Note: After November 1, 2024, this dataset will no longer be updated due to a transition in NHSN Hospital Respiratory Data reporting that occurred on Friday, November 1, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Due to a recent update in voluntary NHSN Hospital Respiratory Data reporting that occurred on Wednesday, October 9, 2024, reporting levels and other data displayed on this page may fluctuate week-over-week beginning Friday, October 18, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient. This dataset represents weekly respiratory virus-related hospitalization data and metrics aggregated to national and state/territory levels reported during two periods: 1) data for collection dates from August 1, 2020 to April 30, 2024, represent data reported by hospitals during a mandated reporting period as specified by the HHS Secretary; and 2) data for collection dates beginning May 1, 2024, represent data reported voluntarily by hospitals to CDC’s National Healthcare Safety Network (NHSN). NHSN monitors national and local trends in healthcare system stress and capacity for up to approximately 6,000 hospitals in the United States. Data reported represent aggregated counts and include metrics capturing information specific to COVID-19- and influenza-related hospitalizations, hospital occupancy, and hospital capacity. Find more information about reporting to NHSN at: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Source: COVID-19 hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN).
Data source description (updated October 18, 2024): As of October 9, 2024, Hospital Respiratory Data (HRD; formerly Respiratory Pathogen, Hospital Capacity, and Supply data or ‘COVID-19 hospital data’) are reported to HHS through CDC’s National Healthcare Safety Network based on updated requirements from the Centers for Medicare and Medicaid Services (CMS). These data are voluntarily reported to NHSN as of May 1, 2024 until November 1, 2024, at which time CMS will require acute care and critical access hospitals to electronically report information via NHSN about COVID-19, Influenza, and RSV, hospital bed census and capacity, and limited patient demographic information, including age. Data for collection dates prior to May 1, 2024, represent data reported during a previously mandated reporting period as specified by the HHS Secretary. Data for collection dates May 1, 2024, and onwards represent data reported voluntarily to NHSN; as such, data included represents reporting hospitals only for a given week and might not be complete or representative of all hospitals. NHSN monitors national and local trends in healthcare system stress and capacity for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Find more information about reporting to NHSN: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient.
Data quality: While CDC reviews reported data for completeness and errors and corrects those found, some reporting errors might still exist within the data. CDC and partners work with reporters to correct these errors and update the data in subse
Schema: dwv_pub_health_surv
Table Name: weekly_us_hospitalizations_by_jurisdict__aemt_mg7g
Dataset ID: aemt-mg7g
Category: Public Health Surveillance
Tags: admissions, coronavirus, covid-19, hospital capacity, hospitalizations, hospital occupancy, icu beds, influenza, inpatient beds
Source Data: https://data.cdc.gov/d/aemt-mg7g
Weekly United States Hospitalization Metrics by Jurisdiction, During Mandatory Reporting Period from August 1, 2020 to April 30, 2024, and for Data Reported Voluntarily Beginning May 1, 2024, National Healthcare Safety Network (NHSN) (Historical)-ARCHIVED
Description: Note: After November 1, 2024, this dataset will no longer be updated due to a transition in NHSN Hospital Respiratory Data reporting that occurred on Friday, November 1, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Due to a recent update in voluntary NHSN Hospital Respiratory Data reporting that occurred on Wednesday, October 9, 2024, reporting levels and other data displayed on this page may fluctuate week-over-week beginning Friday, October 18, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient. . This dataset represents weekly respiratory virus-related hospitalization data and metrics aggregated to national and state/territory levels reported during two periods: 1) data for collection dates from August 1, 2020 to April 30, 2024, represent data reported by hospitals during a mandated reporting period as specified by the HHS Secretary; and 2) data for collection dates beginning May 1, 2024, represent data reported voluntarily by hospitals to CDC’s National Healthcare Safety Network (NHSN). NHSN monitors national and local trends in healthcare system stress and capacity for up to approximately 6,000 hospitals in the United States. Data reported represent aggregated counts and include metrics capturing information specific to COVID-19- and influenza-related hospitalizations, hospital occupancy, and hospital capacity. Find more information about reporting to NHSN at: https://www.cdc.gov/nhsn/covid19/hospital-reporting.html Source: COVID-19 hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN).
Data source description(updated October 18, 2024): As of October 9, 2024, Hospital Respiratory Data (HRD; formerly Respiratory Pathogen, Hospital Capacity, and Supply data or ‘COVID-19 hospital data’) are reported to HHS through CDC’s National Healthcare Safety Network based on updated requirements from the Centers for Medicare and Medicaid Services (CMS). These data are voluntarily reported to NHSN as of May 1, 2024 until November 1, 2024, at which time CMS will require acute care and critical access hospitals to electronically report information via NHSN about COVID-19, Influenza, and RSV, hospital bed census and capacity, and limited patient demographic information, including age. Data for collection dates prior to May 1, 2024, represent data reported during a previously mandated reporting period as specified by the HHS Secretary. Data for collection dates May 1, 2024, and onwards represent data reported voluntarily to NHSN; as such, data included represents reporting hospitals only for a given week and might not be complete or representative of all hospitals. NHSN monitors national and local trends in healthcare system stress and capacity for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Find more information about reporting to NHSN: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient.
Data quality: While CDC reviews reported data for completeness and errors and corrects those found, some reporting errors might still exist within the data. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks
Schema: dwv_pub_health_surv
Table Name: weekly_us_hospitalizations_by_jurisdict__ype6_idgy
Dataset ID: ype6-idgy
Category: Public Health Surveillance
Tags: admissions, coronavirus, covid-19, hospital capacity, hospitalizations, hospital occupancy, icu beds, influenza, inpatient beds
Source Data: https://data.cdc.gov/d/ype6-idgy
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