Global Covid-19 Data

Global Covid-19 data on cases, deaths, vaccinations, and more

By Valtteri Kurkela [source]

About this dataset

The dataset is constantly updated and synced hourly to ensure up-to-date information. With over several columns available for analysis and exploration purposes, users can extract valuable insights from this extensive dataset.

Some of the key metrics covered in the dataset include:

1. Vaccinations: The dataset covers total vaccinations administered worldwide as well as breakdowns of people vaccinated per hundred people and fully vaccinated individuals per hundred people.

2. Testing & Positivity: Information on total tests conducted along with new tests conducted per thousand people is provided. Additionally, details on positive rate (percentage of positive Covid-19 tests out of all conducted) are included.

3. Hospital & ICU: Data on ICU patients and hospital patients are available along with corresponding figures normalized per million people. Weekly admissions to intensive care units and hospitals are also provided.

4. Confirmed Cases: The number of confirmed Covid-19 cases globally is captured in both absolute numbers as well as normalized values representing cases per million people.

5.Confirmed Deaths: Total confirmed deaths due to Covid-19 worldwide are provided with figures adjusted for population size (total deaths per million).

6.Reproduction Rate: The estimated reproduction rate (R) indicates the contagiousness of the virus within a particular country or region.

7.Policy Responses: Besides healthcare-related metrics, this comprehensive dataset includes policy responses implemented by countries or regions such as lockdown measures or travel restrictions.

8.Other Variables of InterestThe data encompasses various socioeconomic factors that may influence Covid-19 outcomes including population density,membership in a continent,gross domestic product(GDP)per capita;

For demographic factors: -Age Structure : percentage populations aged 65 and older,aged (70)older,median age -Gender-specific factors: Percentage of female smokers -Lifestyle-related factors: Diabetes prevalence rate and extreme poverty rate

1. Excess Mortality: The dataset further provides insights into excess mortality rates, indicating the percentage increase in deaths above the expected number based on historical data.

The dataset consists of numerous columns providing specific information for analysis, such as ISO code for countries/regions, location names,and units of measurement for different parameters.

Overall,this dataset serves as a valuable resource for researchers, analysts, and policymakers seeking to explore various aspects related to Covid-19

How to use the dataset

Introduction:

• Understanding the Basic Structure:

  • The dataset consists of various columns containing different data related to vaccinations, testing, hospitalization, cases, deaths, policy responses, and other key variables.
  • Each row represents data for a specific country or region at a certain point in time.

• Selecting Desired Columns:

  • Identify the specific columns that are relevant to your analysis or research needs.
  • Some important columns include population, total cases, total deaths, new cases per million people, and vaccination-related metrics.

• Filtering Data:

  • Use filters based on specific conditions such as date ranges or continents to focus on relevant subsets of data.
  • This can help you analyze trends over time or compare data between different regions.

• Analyzing Vaccination Metrics:

  • Explore variables like total_vaccinations, people_vaccinated, and people_fully_vaccinated to assess vaccination coverage in different countries.
  • Calculate metrics such as people_vaccinated_per_hundred or total_boosters_per_hundred for standardized comparisons across populations.

• Investigating Testing Information:

  • Examine columns such as total_tests, new_tests, and tests_per_case to understand testing efforts in various countries.
  • Calculate rates like tests_per_case to assess testing efficiency or identify changes in testing strategies over time.

• Exploring Hospitalization and ICU Data:

  • Analyze variables like hosp_patients, icu_patients, and hospital_beds_per_thousand to understand healthcare systems' strain.
  • Calculate rates like icu_patients_per_million or hosp_patients_per_million for cross-country comparisons.

• Assessing Covid-19 Cases and Deaths:

  • Analyze variables like total_cases, new_ca...

NOTE: This dataset has been retired and marked as historical-only. Weekly rates of COVID-19 cases, hospitalizations, and deaths among people living in Chicago by vaccination status and age. Rates for fully vaccinated and unvaccinated begin the week ending April 3, 2021 when COVID-19 vaccines became widely available in Chicago. Rates for boosted begin the week ending October 23, 2021 after booster shots were recommended by the Centers for Disease Control and Prevention (CDC) for adults 65+ years old and adults in certain populations and high risk occupational and institutional settings who received Pfizer or Moderna for their primary series or anyone who received the Johnson & Johnson vaccine. Chicago residency is based on home address, as reported in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE) and Illinois National Electronic Disease Surveillance System (I-NEDSS). Outcomes: • Cases: People with a positive molecular (PCR) or antigen COVID-19 test result from an FDA-authorized COVID-19 test that was reported into I-NEDSS. A person can become re-infected with SARS-CoV-2 over time and so may be counted more than once in this dataset. Cases are counted by week the test specimen was collected. • Hospitalizations: COVID-19 cases who are hospitalized due to a documented COVID-19 related illness or who are admitted for any reason within 14 days of a positive SARS-CoV-2 test. Hospitalizations are counted by week of hospital admission. • Deaths: COVID-19 cases who died from COVID-19-related health complications as determined by vital records or a public health investigation. Deaths are counted by week of death. Vaccination status: • Fully vaccinated: Completion of primary series of a U.S. Food and Drug Administration (FDA)-authorized or approved COVID-19 vaccine at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Boosted: Fully vaccinated with an additional or booster dose of any FDA-authorized or approved COVID-19 vaccine received at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Unvaccinated: No evidence of having received a dose of an FDA-authorized or approved vaccine prior to a positive test. CLARIFYING NOTE: Those who started but did not complete all recommended doses of an FDA-authorized or approved vaccine prior to a positive test (i.e., partially vaccinated) are excluded from this dataset. Incidence rates for fully vaccinated but not boosted people (Vaccinated columns) are calculated as total fully vaccinated but not boosted with outcome divided by cumulative fully vaccinated but not boosted at the end of each week. Incidence rates for boosted (Boosted columns) are calculated as total boosted with outcome divided by cumulative boosted at the end of each week. Incidence rates for unvaccinated (Unvaccinated columns) are calculated as total unvaccinated with outcome divided by total population minus cumulative boosted, fully, and partially vaccinated at the end of each week. All rates are multiplied by 100,000. Incidence rate ratios (IRRs) are calculated by dividing the weekly incidence rates among unvaccinated people by those among fully vaccinated but not boosted and boosted people. Overall age-adjusted incidence rates and IRRs are standardized using the 2000 U.S. Census standard population. Population totals are from U.S. Census Bureau American Community Survey 1-year estimates for 2019. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. This dataset reflects data known to CDPH at the time when the dataset is updated each week. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. For all datasets related to COVID-19, see https://data.cityofchic

As of 09/24/24 this dataset will be retired and no longer updated.

This data set includes the cumulative (total) number of COVID-19 cases, hospitalizations, deaths, outbreaks, testing percent positivity and MIS-C in Virginia. This data set was first published on November 01, 2020. The data set increases in size daily and as a result, the dataset may take longer to update; however, it is expected to be available by 12:00 noon.

Daily count of NYC residents who tested positive for SARS-CoV-2, who were hospitalized with COVID-19, and deaths among COVID-19 patients.

Note that this dataset currently pulls from https://raw.githubusercontent.com/nychealth/coronavirus-data/master/case-hosp-death.csv on a daily basis.

Context

There are two datasets. 1. owid-covid-data.csv :- Contains covid data from 1st Jan 2020 to 7th Feb, 2023 2. owid-covid-latest.csv:- Contains covid data from 8th Feb, 2023.

Content

Dataset Attribute Details:

iso_code: ISO 3166-1 alpha-3 – three-letter country codes continent: Continent of the geographical location location: Geographical location date: Date of observation total_cases: Total confirmed cases of COVID-19 new_cases: New confirmed cases of COVID-19 new_cases_smoothed: New confirmed cases of COVID-19 (7-day smoothed) total_cases_per_million: Total confirmed cases of COVID-19 per 1,000,000 people new_cases_per_million: New confirmed cases of COVID-19 per 1,000,000 people new_cases_smoothed_per_million: New confirmed cases of COVID-19 (7-day smoothed) per 1,000,000 people total_deaths: Total deaths attributed to COVID-19 new_deaths: New deaths attributed to COVID-19 new_deaths_smoothed: New deaths attributed to COVID-19 (7-day smoothed) total_deaths_per_million: Total deaths attributed to COVID-19 per 1,000,000 people new_deaths_per_million: New deaths attributed to COVID-19 per 1,000,000 people new_deaths_smoothed_per_million: New deaths attributed to COVID-19 (7-day smoothed) per 1,000,000 people excess_mortality: Percentage difference between the reported number of weekly or monthly deaths in 2020–2021 and the projected number of deaths for the same period based on previous years. excess_mortality_cumulative: Percentage difference between the cumulative number of deaths since 1 January 2020 and the cumulative projected deaths for the same period based on previous years. excess_mortality_cumulative_absolute: Cumulative difference between the reported number of deaths since 1 January 2020 and the projected number of deaths for the same period based on previous years. excess_mortality_cumulative_per_million: Cumulative difference between the reported number of deaths since 1 January 2020 and the projected number of deaths for the same period based on previous years, per million people. icu_patients: Number of COVID-19 patients in intensive care units (ICUs) on a given day icu_patients_per_million: Number of COVID-19 patients in intensive care units (ICUs) on a given day per 1,000,000 people hosp_patients: Number of COVID-19 patients in the hospital on a given day hosp_patients_per_million: Number of COVID-19 patients in hospital on a given day per 1,000,000 people weekly_icu_admissions: Number of COVID-19 patients newly admitted to intensive care units (ICUs) in a given week weekly_icu_admissions_per_million: Number of COVID-19 patients newly admitted to intensive care units (ICUs) in a given week per 1,000,000 people weekly_hosp_admissions: Number of COVID-19 patients newly admitted to hospitals in a given week weekly_hosp_admissions_per_million: Number of COVID-19 patients newly admitted to hospitals in a given week per 1,000,000 people stringency_index: Government Response Stringency Index: composite measure based on 9 response indicators including school closures, workplace closures, and travel bans, rescaled to a value from 0 to 100 (100 = strictest response) reproduction_rate: Real-time estimate of the effective reproduction rate (R) of COVID-19. total_tests: Total tests for COVID-19 new_tests: New tests for COVID-19 (only calculated for consecutive days) total_tests_per_thousand: Total tests for COVID-19 per 1,000 people new_tests_per_thousand: New tests for COVID-19 per 1,000 people new_tests_smoothed: New tests for COVID-19 (7-day smoothed). For countries that don't report testing data on a daily basis, we assume that testing changed equally on a daily basis over any periods in which no data was reported. This produces a complete series of daily figures, which is then averaged over a rolling 7-day window new_tests_smoothed_per_thousand: New tests for COVID-19 (7-day smoothed) per 1,000 people positive_rate: The share of COVID-19 tests that are positive, given as a rolling 7-day average (this is the inverse of tests_per_case) tests_per_case: Tests conducted per new confirmed case of COVID-19, given as a rolling 7-day average (this is the inverse of positive_rate) tests_units: Units used by the location to report its testing data total_vaccinations: Total number of COVID-19 vaccination doses administered people_vaccinated: Total number of people who received at least one vaccine dose people_fully_vaccinated: Total number of people who received all doses prescribed by the vaccination protocol total_boosters: Total number of COVID-19 vaccination booster doses administered (doses administered beyond the number prescribed by the vaccination protocol) new_vaccinations: New COVID-19 vaccination doses a...

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

Status of COVID-19 cases in Ontario

This dataset compiles daily snapshots of publicly reported data on 2019 Novel Coronavirus (COVID-19) testing in Ontario.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak.

Effective April 13, 2023, this dataset will be discontinued. The public can continue to access the data within this dataset in the following locations updated weekly on the Ontario Data Catalogue:

• Ontario COVID-19 testing percent positive by age group
• Confirmed positive cases of COVID-19 in Ontario
• Ontario COVID-19 testing metrics by Public Health Unit (PHU)
• Ontario COVID-19 testing percent positive by age group
• COVID-19 cases in hospital and ICU, by Ontario Health (OH) region
• Cumulative deaths (new methodology)
• Deaths Involving COVID-19 by Fatality Type

For information on Long-Term Care Home COVID-19 Data, please visit: Long-Term Care Home COVID-19 Data.

Data includes:

• reporting date
• daily tests completed
• total tests completed
• test outcomes
• total case outcomes (resolutions and deaths)
• current tests under investigation
• current hospitalizations
  • current patients in Intensive Care Units (ICUs) due to COVID-related critical Illness
  • current patients in Intensive Care Units (ICUs) testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) no longer testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) on ventilators due to COVID-related critical illness
  • current patients in Intensive Care Units (ICUs) on ventilators testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) on ventilators no longer testing positive for COVID-19
• Long-Term Care (LTC) resident and worker COVID-19 case and death totals
• Variants of Concern case totals
• number of new deaths reported (occurred in the last month)
• number of historical deaths reported (occurred more than one month ago)
• change in number of cases from previous day by Public Health Unit (PHU).

This dataset is subject to change. Please review the daily epidemiologic summaries for information on variables, methodology, and technical considerations.

Cumulative Deaths

**Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool **

The methodology used to count COVID-19 deaths has changed to exclude deaths not caused by COVID. This impacts data captured in the columns “Deaths”, “Deaths_Data_Cleaning” and “newly_reported_deaths” starting with data for March 11, 2022. A new column has been added to the file “Deaths_New_Methodology” which represents the methodological change.

The method used to count COVID-19 deaths has changed, effective December 1, 2022. Prior to December 1, 2022, deaths were counted based on the date the death was updated in the public health unit’s system. Going forward, deaths are counted on the date they occurred.

On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. A small number of COVID deaths (less than 20) do not have recorded death date and will be excluded from this file.

CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags.

Related dataset(s)

• Confirmed positive cases of COVID-19 in Ontario

DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

COVID-19 state summary including the following metrics, including the change from the data reported the previous day:

COVID-19 Cases (confirmed and probable) COVID-19 Tests Reported (molecular and antigen) Daily Test Positivity Patients Currently Hospitalized with COVID-19 COVID-19-Associated Deaths

Additional notes: The cumulative count of tests reported for 1/17/2021 includes 286,103 older tests from previous dates, which had been missing from previous reports due to a data processing error. The older tests were added to the cumulative count of tests reported, but they were not included in the calculation of change from the previous reporting day or daily percent test positivity.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

Starting April 4, 2022, negative rapid antigen and rapid PCR test results for SARS-CoV-2 are no longer required to be reported to the Connecticut Department of Public Health as of April 4. Negative test results from laboratory based molecular (PCR/NAAT) results are still required to be reported as are all positive test results from both molecular (PCR/NAAT) and antigen tests.

Nursing homes with residents positive for COVID-19 from 4/22/2020 to 6/19/2020.

Starting in July 2020, this dataset will no longer be updated and will be replaced by the CMS COVID-19 Nursing Home Dataset, available at the following link: https://data.ct.gov/Health-and-Human-Services/CMS-COVID-19-Nursing-Home-Dataset/w8wc-65i5.

Methods: 1) Laboratory-confirmed case counts are based upon data reported via the FLIS web portal. Nursing homes were asked to provide cumulative totals of residents with laboratory confirmed covid. This includes residents currently in-house, in the hospital, or who are deceased. Residents were excluded if they tested positive prior to initial admission to the nursing home. 2) The cumulative number of deaths among nursing home residents is based upon data reported by the Office of the Chief Medical Examiner. For public health surveillance, COVID-19-associated deaths include persons who tested positive for COVID-19 around the time of death (laboratory-confirmed) and persons whose death certificate lists COVID-19 disease as a cause of death or a significant condition contributing to death (probable).

Limitations: 1) As of the week of 5/10/20, Point Prevalence Survey testing is being offered to all asymptomatic nursing home residents to inform infection prevention efforts. Point prevalence surveys will be conducted over a period of several weeks. Some nursing homes had adequate testing resources available to conduct surveys prior to this date. Differences in survey timing will impact the number of positive results that a nursing home reports. 2) Cumulative totals of residents testing positive are being collected rather than individual resident data. Thus we cannot verify the counts, de-duplicate, and/or verify whether there is a record of a positive lab test. This may result in either under- or over-counting. 3) The number of COVID-19 positive residents and the number of confirmed deaths among residents are tabulated from different data sources. Due to the timing of availability of test results for deceased residents, it is not appropriate to calculate the percent of cases who died due to COVID-19 at any particular facility based upon this data. 4) The count of deaths reported for 4/14 are not included in this dataset, as they were not broken out by laboratory-confirmed or probable. They can be viewed in the DPH Report here: https://portal.ct.gov/-/media/Coronavirus/CTDPHCOVID19summary4162020.pdf?la=en

**Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool **

As of January 26, 2023, the population counts are based on Statistics Canada’s 2021 estimates. The coverage methodology has been revised to calculate age based on the current date and deceased individuals are no longer included. The method used to count daily dose administrations has changed is now based on the date delivered versus the day entered into the data system. Historical data has been updated.

Please note that Cases by Vaccination Status data will no longer be published as of June 30, 2022.

Please note that case rates by vaccination status and age group data will no longer be published as of July 13, 2022.

Please note that Hospitalization by Vaccination Status data will no longer be published as of June 30, 2022.

Learn more about COVID-19 vaccines.

Data includes:

• daily and total doses administered
• individuals with at least one dose
• individuals fully vaccinated
• total doses given to fully vaccinated individuals
• vaccinations by age
• percentage of age group
• individuals with at least one dose, by PHU, by age group
• individuals fully vaccinated, by PHU, by age group
• COVID-19 cases by status: not fully vaccinated, fully vaccinated, vaccinated with booster
• individuals in hospital due to COVID-19 (excluding ICU) by status: unvaccinated, partially vaccinated, fully vaccinated
• individuals in ICU due to COVID-19 by status: unvaccinated, partially vaccinated, fully vaccinated, unknown
• rate of COVID-19 cases per 100,000 by status and age group
• rate per 100,000 (7-day average) by status and age group

All data reflects totals from 8 p.m. the previous day.

This dataset is subject to change.

Additional notes

• Data entry of vaccination records is still in progress, therefore the dosage data may not be a full representation of all vaccination doses administered in Ontario.
• The data does not include dosage data where consent was not provided for vaccination records to be entered into the provincial CoVax system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information into CoVax.

Hospitalizations and cases by vaccination status

Hospitalizations

• This is a new data collection and the data quality will continue to improve as hospitals continue to submit data.
• In order to understand the vaccination status of patients currently hospitalized, a new data collection process was developed and this may cause discrepancies between other hospitalization numbers being collected using a different data collection process.
• Data on patients in ICU are being collected from two different data sources with different extraction times and public reporting cycles. The existing data source (Critical Care Information System, CCIS) does not have vaccination status.
• Historical data for hospitalizations by region may change over time as hospitals update previously entered data.
• Due to incomplete weekend and holiday reporting, vaccination status data for hospital and ICU admissions is not updated on Sundays, Mondays and the day after holidays
• Unvaccinated is defined as not having any dose, or between 0-13 days after administration of the first dose of a COVID-19 vaccine.
• Partially vaccinated is defined as 14 days or more after the first dose of a 2-dose series COVID-19 vaccine, or between 0-13 days after administration of the second dose
• Fully vaccinated is defined as 14 days or more after receipt of the second dose of a 2-dose series COVID-19 vaccine

Cases

• The cases by vaccination status may not match the daily COVID-19 case count because records with a missing or invalid health card number cannot be linked.

Coronavirus Disease (COVID-19) statistics data from Ministerio de Sanidad, Consumo y Bienestar Social, ordered by days and Spanish regions. National level variables in file nacional_covid19.csv: Date of notification, Accumulated confirmed cases, Accumulated recovered, Accumulated deceased, Accumulated cases that have required hospitalization (include admitted to the IC), Accumulated cases that have required admission to the IC National level variables in file nacional_covid19_rango_edad.csv: Date of notification, age range, gender, Accumulated confirmed cases (Data obtained from the analysis of a daily variable percentage of cases reported), Accumulated cases that have required hospitalization (include admitted to the IC) (Data obtained from the analysis of a daily variable percentage of cases reported), Accumulated cases that have required admission to the IC (Data obtained from the analysis of a daily variable percentage of cases reported), Accumulated deceased (Data obtained from the analysis of a daily variable percentage of cases reported) Comunidad Autónoma level variables: Date of notification, National Statistics Institute code of the autonomous community, Autonomous community, confirmed cases registered, deceased cases registered, Cases that have required hospitalization (include admitted to the IC), Cases that have required admission to the IC, Accumulated recovered cases

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 among people who received additional or booster doses were reported from 31 jurisdictions; 30 jurisdictions also reported data on deaths among people who received one or more additional or booster dose; 28 jurisdictions reported cases among people who received two or more additional or booster doses; and 26 jurisdictions reported deaths among people who received two or more additional or booster doses. This list will be updated as more jurisdictions participate. Incidence rate estimates: Weekly age-specific incidence rates by vaccination status were calculated as the number of cases or deaths divided by the number of people vaccinated with a primary series, overall or with/without a booster dose (cumulative) or unvaccinated (obtained by subtracting the cumulative number of people vaccinated with a primary series and partially vaccinated people from the 2019 U.S. intercensal population estimates) and multiplied by 100,000. Overall incidence rates were age-standardized using the 2000 U.S. Census standard population. To estimate population counts for ages 6 months through 1 year, half of the single-year population counts for ages 0 through 1 year were used. All rates are plotted by positive specimen collection date to reflect when incident infections occurred. For the primary series analysis, age-standardized rates include ages 12 years and older from April 4, 2021 through December 4, 2021, ages 5 years and older from December 5, 2021 through July 30, 2022 and ages 6 months and older from July 31, 2022 onwards. For the booster dose analysis, age-standardized rates include ages 18 years and older from September 19, 2021 through December 25, 2021, ages 12 years and older from December 26, 2021, and ages 5 years and older from June 5, 2022 onwards. Small numbers could contribute to less precision when calculating death rates among some groups. 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 incidence and death rates from growing unrealistically large due to potential overestimates of vaccination coverage. Incidence rate ratios (IRRs): IRRs for the past one month were calculated by dividing the average weekly incidence rates among unvaccinated people by that among people vaccinated with a primary series either overall or with a booster dose. Publications: Scobie HM, Johnson AG, Suthar AB, et al. Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status — 13 U.S. Jurisdictions, April 4–July 17, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1284–1290. Johnson AG, Amin AB, Ali AR, et al. COVID-19 Incidence and Death Rates Among Unvaccinated and Fully Vaccinated Adults with and Without Booster Doses During Periods of Delta and Omicron Variant Emergence — 25 U.S. Jurisdictions, April 4–December 25, 2021. MMWR Morb Mortal Wkly Rep 2022;71:132–138

Objectives: The present study is aimed at estimating patient flow dynamic parameters and requirement for hospital beds. Second, the effects of age and gender on parameters were evaluated.Patients and Methods: In this retrospective cohort study, 987 COVID-19 patients were enrolled from SMS Medical College, Jaipur (Rajasthan, India). The survival analysis was carried out from February 29 through May 19, 2020, for two hazards: Hazard 1 was hospital discharge, and Hazard 2 was hospital death. The starting point for survival analysis of the two hazards was considered to be hospital admission. The survival curves were estimated and additional effects of age and gender were evaluated using Cox proportional hazard regression analysis.Results: The Kaplan Meier estimates of lengths of hospital stay (median = 10 days, IQR = 5–15 days) and median survival rate (more than 60 days due to a large amount of censored data) were obtained. The Cox model for Hazard 1 showed no significant effect of age and gender on duration of hospital stay. Similarly, the Cox model 2 showed no significant difference of age and gender on survival rate. The case fatality rate of 8.1%, recovery rate of 78.8%, mortality rate of 0.10 per 100 person-days, and hospital admission rate of 0.35 per 100,000 person-days were estimated.Conclusion: The study estimates hospital bed requirements based on median length of hospital stay and hospital admission rate. Furthermore, the study concludes there are no effects of age and gender on average length of hospital stay and no effects of age and gender on survival time in above-60 age groups.

The COVID-19 dashboard includes data on city/town COVID-19 activity, confirmed and probable cases of COVID-19, confirmed and probable deaths related to COVID-19, and the demographic characteristics of cases and deaths.

Estimated durations of the active periods of COVID-19 outbreaks in nursing homes, estimated delays and values of the outbreak probability peaks in the nursing homes and the hospitalization probability peaks for COVID-19 in the populations of Auvergne-Rhône-Alpes Region Départements, France, March 1–July 31, 2020.

Total Hospitalizations, ICU admissions and deaths (ever) among COVID-19 cases in Alberta by age group, from Table 7 at https://www.alberta.ca/stats/covid-19-alberta-statistics.htm#severe-outcomes.

Based on total hospitalizations and ICU admissions ever. Row percent is out of the number of cases in each age group. Each ICU admission is also included in the total number of hospitalization Case rate (per 100 cases) Population rate (per 100,000 population)

This dataset was last updated 2023-08-29 15:15 with data as of end of day 2023-07-24.

These indicators are designed to accompany the SHMI publication. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. There has been a fall in the number of spells for some trusts due to COVID-19 impacting on activity from March 2020 onwards and this appears to be an accurate reflection of hospital activity rather than a case of missing data. Contextual indicators on the number of provider spells which are excluded from the SHMI due to them being related to COVID-19 and on the number of provider spells as a percentage of pre-pandemic activity (January 2019 – December 2019) are produced to support the interpretation of the SHMI. These indicators are being published as experimental statistics. Experimental statistics are official statistics which are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. Notes: 1. There is a shortfall in the number of records for The Princess Alexandra Hospital NHS Trust (trust code RQW). Values for this trust are based on incomplete data and should therefore be interpreted with caution. 2. Frimley Health NHS Foundation Trust (trust code RDU) stopped submitting data to the Secondary Uses Service (SUS) during June 2022 and did not start submitting data again until April 2023 due to an issue with their patient records system. This is causing a large shortfall in records and values for this trust should be viewed in the context of this issue. 3. There is a high percentage of invalid diagnosis codes for Chesterfield Royal Hospital NHS Foundation Trust (trust code RFS), Milton Keynes University Hospital NHS Foundation Trust (trust code RD8), and West Suffolk NHS Foundation Trust (trust code RGR). Values for these trusts should therefore be interpreted with caution. 4. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the Background Quality Report. 5. East Kent Hospitals University NHS Foundation Trust (trust code RVV) has a submission issue which is causing many of their patient spells to be duplicated in the HES Admitted Patient Care data. This means that the number of spells for this trust in this dataset are overstated by approximately 60,000, and the trust’s SHMI value will be lower as a result. Values for this trust should therefore be interpreted with caution. 6. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.

Status of COVID-19 cases in Ontario This dataset compiles daily snapshots of publicly reported data on 2019 Novel Coronavirus (COVID-19) testing in Ontario. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. Effective April 13, 2023, this dataset will be discontinued. The public can continue to access the data within this dataset in the following locations updated weekly on the Ontario Data Catalogue: * Ontario COVID-19 testing percent positive by age group * Confirmed positive cases of COVID-19 in Ontario * Ontario COVID-19 testing metrics by Public Health Unit (PHU) * Ontario COVID-19 testing percent positive by age group * COVID-19 cases in hospital and ICU, by Ontario Health (OH) region * Cumulative deaths (new methodology) * Deaths Involving COVID-19 by Fatality Type For information on Long-Term Care Home COVID-19 Data, please visit: Long-Term Care Home COVID-19 Data. Data includes: * reporting date * daily tests completed * total tests completed * test outcomes * total case outcomes (resolutions and deaths) * current tests under investigation * current hospitalizations * current patients in Intensive Care Units (ICUs) due to COVID-related critical Illness * current patients in Intensive Care Units (ICUs) testing positive for COVID-19 * current patients in Intensive Care Units (ICUs) no longer testing positive for COVID-19 * current patients in Intensive Care Units (ICUs) on ventilators due to COVID-related critical illness * current patients in Intensive Care Units (ICUs) on ventilators testing positive for COVID-19 * current patients in Intensive Care Units (ICUs) on ventilators no longer testing positive for COVID-19 * Long-Term Care (LTC) resident and worker COVID-19 case and death totals * Variants of Concern case totals * number of new deaths reported (occurred in the last month) * number of historical deaths reported (occurred more than one month ago) * change in number of cases from previous day by Public Health Unit (PHU). This dataset is subject to change. Please review the daily epidemiologic summaries for information on variables, methodology, and technical considerations. ##Cumulative Deaths Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool The methodology used to count COVID-19 deaths has changed to exclude deaths not caused by COVID. This impacts data captured in the columns “Deaths”, “Deaths_Data_Cleaning” and “newly_reported_deaths” starting with data for March 11, 2022. A new column has been added to the file “Deaths_New_Methodology” which represents the methodological change. The method used to count COVID-19 deaths has changed, effective December 1, 2022. Prior to December 1, 2022, deaths were counted based on the date the death was updated in the public health unit’s system. Going forward, deaths are counted on the date they occurred. On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. A small number of COVID deaths (less than 20) do not have recorded death date and will be excluded from this file. CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags. ##Related dataset(s) * Confirmed positive cases of COVID-19 in Ontario

These indicators are designed to accompany the SHMI publication. Information on the main condition the patient is in hospital for (the primary diagnosis) is used to calculate the expected number of deaths used in the calculation of the SHMI. A high percentage of records with an invalid primary diagnosis may indicate a data quality problem. A high percentage of records with a primary diagnosis which is a symptom or sign may indicate problems with data quality or timely diagnosis of patients, but may also reflect the case-mix of patients or the service model of the trust (e.g. a high level of admissions to acute admissions wards for assessment and stabilisation). Contextual indicators on the percentage of provider spells with an invalid primary diagnosis and the percentage of provider spells with a primary diagnosis which is a symptom or sign are produced to support the interpretation of the SHMI. Notes: 1. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. Activity that is being coded as COVID-19, and therefore excluded, is monitored in the contextual indicator 'Percentage of provider spells with COVID-19 coding' which is part of this publication. 2. Please note that there was a fall in the overall number of spells from March 2020 due to COVID-19 impacting on activity for England and the number has not returned to pre-pandemic levels. Further information at Trust level is available in the contextual indicator ‘Provider spells compared to the pre-pandemic period’ which is part of this publication. 3. There is a shortfall in the number of records for The Princess Alexandra Hospital NHS Trust (trust code RQW). Values for this trust are based on incomplete data and should therefore be interpreted with caution. 4. Frimley Health NHS Foundation Trust (trust code RDU) stopped submitting data to the Secondary Uses Service (SUS) during June 2022 and did not start submitting data again until April 2023 due to an issue with their patient records system. This is causing a large shortfall in records and values for this trust should be viewed in the context of this issue. 5. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the Background Quality Report. 6. East Kent Hospitals University NHS Foundation Trust (trust code RVV) has a submission issue which is causing many of their patient spells to be duplicated in the HES Admitted Patient Care data. This means that the number of spells for this trust in this dataset are overstated by approximately 60,000, and the trust’s SHMI value will be lower as a result. Values for this trust should therefore be interpreted with caution. 7. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of this page.

BackgroundThe global outbreak of coronavirus disease 2019 (COVID-19) has turned into a worldwide public health crisis and caused more than 100,000,000 severe cases. Progressive lymphopenia, especially in T cells, was a prominent clinical feature of severe COVID-19. Activated HLA-DR+CD38+ CD8+ T cells were enriched over a prolonged period from the lymphopenia patients who died from Ebola and influenza infection and in severe patients infected with SARS-CoV-2. However, the CD38+HLA-DR+ CD8+ T population was reported to play contradictory roles in SARS-CoV-2 infection.MethodsA total of 42 COVID-19 patients, including 32 mild or moderate and 10 severe or critical cases, who received care at Beijing Ditan Hospital were recruited into this retrospective study. Blood samples were first collected within 3 days of the hospital admission and once every 3–7 days during hospitalization. The longitudinal flow cytometric data were examined during hospitalization. Moreover, we evaluated serum levels of 45 cytokines/chemokines/growth factors and 14 soluble checkpoints using Luminex multiplex assay longitudinally.ResultsWe revealed that the HLA-DR+CD38+ CD8+ T population was heterogeneous, and could be divided into two subsets with distinct characteristics: HLA-DR+CD38dim and HLA-DR+CD38hi. We observed a persistent accumulation of HLA-DR+CD38hi CD8+ T cells in severe COVID-19 patients. These HLA-DR+CD38hi CD8+ T cells were in a state of overactivation and consequent dysregulation manifested by expression of multiple inhibitory and stimulatory checkpoints, higher apoptotic sensitivity, impaired killing potential, and more exhausted transcriptional regulation compared to HLA-DR+CD38dim CD8+ T cells. Moreover, the clinical and laboratory data supported that only HLA-DR+CD38hi CD8+ T cells were associated with systemic inflammation, tissue injury, and immune disorders of severe COVID-19 patients.ConclusionsOur findings indicated that HLA-DR+CD38hi CD8+ T cells were correlated with disease severity of COVID-19 rather than HLA-DR+CD38dim population.