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

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

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. 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 Data includes: * Date on which the death occurred * Age group * 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster ##Additional notes As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm. As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category. 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. 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. The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON. “Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results. Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts. Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different. Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported. Rates for the most recent days are subject to reporting lags All data reflects totals from 8 p.m. the previous day. This dataset is subject to change.

Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

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

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...

Context

Why we need to compare the rates of death between vaccinated and unvaccinated During a pandemic, you might see headlines like “Half of those who died from the virus were vaccinated”.

It would be wrong to draw any conclusions about whether the vaccines are protecting people from the virus based on this headline. The headline is not providing enough information to draw any conclusions.

Content

Data comes from https://ourworldindata.org/covid-deaths-by-vaccination Thanks to them to compile thiese kind of interesting dataset. If you want to know more please visit https://ourworldindata.org/covid-deaths-by-vaccination

Acknowledgements

https://www.pya.org/Content/Image/NewsBlog/Covid19%20vaccine.jpg" alt="Covid19 vaccination">

Inspiration

Exploration Data, Forecasting, Impact of vaccination in USA. Compare Moderna vs Johnson&Johnson vs Moderna

Description:

This comprehensive dataset provides global information on both COVID-19 related deaths and vaccinations from January 5, 2020, to August 4, 2024. It consists of two parts: one tracking COVID-19 cases, deaths, and population statistics, and another monitoring vaccination progress worldwide. This dataset allows for an in-depth analysis of the pandemic’s spread, fatality rates, and the effectiveness of vaccination campaigns across various countries and regions.

Researchers and data analysts can use this dataset to study trends, compare countries, and evaluate public health responses throughout the COVID-19 pandemic.

Includes:

CovidDeaths Dataset: Records of total cases, deaths, and population.

CovidVaccinations Dataset: Records of daily vaccination counts and cumulative totals.

Use Cases:

Analyzing death rates relative to confirmed cases. Examining the percentage of population affected by COVID-19. Evaluating vaccination rates and coverage across different regions. This dataset is ideal for data exploration, statistical analysis, and visualizations related to the COVID-19 pandemic.

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 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 at least 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 6-12 months, half of the single-year population counts for ages <12 months were used. All rates are plotted by positive specimen collection date to reflect when incident infections occurred.

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 without an updated (bivalent) booster dose) or vaccinated with an updated (bivalent) booster dose.

Archive: An archive of historic data, including April 3, 2021-September 24, 2022 and posted on October 21, 2022 is available on data.cdc.gov. The analysis by vaccination status (unvaccinated and at least a primary series) for 31 jurisdictions is posted here: https://data.cdc.gov/Public-Health-Surveillance/Rates-of-COVID-19-Cases-or-Deaths-by-Age-Group-and/3rge-nu2a. The analysis for one booster dose (unvaccinated, primary series only, and at least one booster dose) in 31 jurisdictions is posted here: https://data.cdc.gov/Public-Health-Surveillance/Rates-of-COVID-19-Cases-or-Deaths-by-Age-Group-and/d6p8-wqjm. The analysis for two booster doses (unvaccinated, primary series only, one booster dose, and at least two booster doses) in 28 jurisdictions is posted here: https://data.cdc.gov/Public-Health-Surveillance/Rates-of-COVID-19-Cases-or-Deaths-by-Age-Group-and/ukww-au2k.

References

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

Johnson AG, Linde L, Ali AR, et al. COVID-19 Incidence and Mortality Among Unvaccinated and Vaccinated Persons Aged ≥12 Years by Receipt of Bivalent Booster Doses and Time Since Vaccination — 24 U.S. Jurisdictions, October 3, 2021–December 24, 2022. MMWR Morb Mortal Wkly Rep 2023;72:145–152

This dataset tracks the updates made on the dataset "Rates of COVID-19 Cases or Deaths by Age Group and Vaccination Status" as a repository for previous versions of the data and metadata.

While some studies have previously estimated lives saved by COVID-19 vaccination, we estimate how many deaths could have been averted by vaccination in the US but were not because of a failure to vaccinate. We used a simple method based on a nationally representative dataset to estimate the preventable deaths among unvaccinated individuals in the US from May 30, 2021 to September 3, 2022 adjusted for the effects of age and time. We estimated that at least 232,000 deaths could have been prevented among unvaccinated adults during the 15 months had they been vaccinated with at least a primary series. While uncertainties exist regarding the exact number of preventable deaths and more granular data are needed on other factors causing differences in death rates between the vaccinated and unvaccinated groups to inform these estimates, this method is a rapid assessment on vaccine-preventable deaths due to SARS-CoV-2 that has crucial public health implications. The same rapid method can be used for future public health emergencies.

Coronavirus (COVID-19) vaccination rates for people aged 18 years and over in England. Estimates by socio-demographic characteristic, region and local authority.

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. 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 Data includes: * Date on which the death occurred * Age group * 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster ##Additional notes As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm. As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category. 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. 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. The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON. “Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results. Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts. Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different. Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported. Rates for the most recent days are subject to reporting lags All data reflects totals from 8 p.m. the previous day. This dataset is subject to change.

This dataset contains two files that provide detailed information on Covid-19 deaths and vaccinations worldwide. The first file contains data on the number of Covid-19 deaths, including total deaths and new deaths, across different locations and time periods. The second file contains data on Covid-19 vaccinations, including total vaccinations, people vaccinated, people fully vaccinated, and total boosters, across different locations and time periods. By analyzing this data, you can uncover insights into the global impact of Covid-19 and explore the relationship between vaccinations and deaths. This dataset is a valuable resource for researchers, data analysts, and anyone interested in understanding the ongoing pandemic.

COVID DEATHS - iso_code: The ISO 3166-1 alpha-3 code of the country or territory. - continent: The continent of the location. - location: The name of the country or territory. - date: The date of the observation. - population: The population of the country or territory. - total_cases: The total number of confirmed cases of Covid-19. - new_cases: The number of new confirmed cases of Covid-19. - new_cases_smoothed: The 7-day smoothed average of new confirmed cases of Covid-19. - total_deaths: The total number of deaths due to Covid-19. - new_deaths: The number of new deaths due to Covid-19. - new_deaths_smoothed: The 7-day smoothed average of new deaths due to Covid-19. - total_cases_per_million: The total number of confirmed cases of Covid-19 per million people. - new_cases_per_million: The number of new confirmed cases of Covid-19 per million people. - new_cases_smoothed_per_million: The 7-day smoothed average of new confirmed cases of Covid-19 per million people. - total_deaths_per_million: The total number of deaths due to Covid-19 per million people. - new_deaths_per_million: The number of new deaths due to Covid-19 per million people. - new_deaths_smoothed_per_million: The 7-day smoothed average of new deaths due to Covid-19 per million people. - reproduction_rate: The estimated average number of people each infected person infects (the "R" number). - icu_patients: The number of patients in intensive care units (ICU) with Covid-19 on the given date. - icu_patients_per_million: The number of patients in intensive care units (ICU) with Covid-19 on the given date, per million people. - hosp_patients: The number of patients in hospital with Covid-19 on the given date. - hosp_patients_per_million: The number of patients in hospital with Covid-19 on the given date, per million people. - weekly_icu_admissions: The weekly number of patients admitted to intensive care units (ICU) with Covid-19. - weekly_icu_admissions_per_million: The weekly number of patients admitted to intensive care units (ICU) with Covid-19, per million people. - weekly_hosp_admissions: The weekly number of patients admitted to hospital with Covid-19. - weekly_hosp_admissions_per_million: The weekly number of patients admitted to hospital with Covid-19, per million people.

COVID VACCINATIONS

• total_tests: The total number of tests for Covid-19.
• new_tests: The number of new tests for Covid-19.
• total_tests_per_thousand: The total number of tests for Covid-19 per thousand people.
• new_tests_per_thousand: The number of new tests for Covid-19 per thousand people.
• new_tests_smoothed: The 7-day smoothed average of new tests for Covid-19.
• new_tests_smoothed_per_thousand: The 7-day smoothed average of new tests for Covid-19 per thousand people.
• positive_rate: The share of Covid-19 tests that are positive, given as a rolling 7-day average.
• tests_per_case: The number of tests conducted per confirmed case of Covid-19, given as a rolling 7-day average.
• tests_units: The units used by the location to report its testing data.
• total_vaccinations: The total number of doses of Covid-19 vaccines administered.
• people_vaccinated: The total number of people who have received at least one dose of a Covid-19 vaccine.
• people_fully_vaccinated: The total number of people who have received all doses prescribed by the vaccination protocol.
• total_boosters: The total number of booster doses administered (doses administered after the prescribed number of doses for full vaccination).
• new_vaccinations: The number of doses of Covid-19 vaccines administered on the given date.
• new_vaccinations_smoothed: The 7-day smoothed average of new doses of Covid-19 vaccines administered.
• total_vaccinations_per_hundred: The total number of doses of Covid-19 vaccines administered per hundred people in the total population.
• people_vaccinated_per_hundred: The total number of people who have received at least one dose of a Covid-19 vaccine per hundred people in the total population.
• people_fully_vaccinated_per_hundred: The total number of people who hav...

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

1. 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 
2. 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
3. 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 
4. 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.
5. 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 
6. 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  
7. 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.
8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
9. https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html 
10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

Context

The COVID-19 outbreak has brought the whole planet to its knees.More over 4.5 million people have died since the writing of this notebook, and the only acceptable way out of the disaster is to vaccinate all parts of society. Despite the fact that the benefits of vaccination have been proved to the world many times, anti-vaccine groups are springing up all over the world. This data set was generated to investigate the impact of coronavirus vaccinations on coronavirus mortality.

Content

Data Collection

This dataset is a combination of the following three datasets:

1.https://www.kaggle.com/gpreda/covid-world-vaccination-progress

2.https://covid19.who.int/WHO-COVID-19-global-data.csv

3.https://www.kaggle.com/rsrishav/world-population

you can find more detail about this dataset by reading this notebook:

https://www.kaggle.com/sinakaraji/simple-linear-regression-covid-vaccination

Countries in this dataset:

Vaccinations in London Between 8 December 2020 and 15 September 2021 5,838,305 1st doses and 5,232,885 2nd doses have been administered to London residents.

Differences in vaccine roll out between London and the Rest of England London Rest of England Priority Group Vaccinations given Percentage vaccinated Vaccinations given Percentage vaccinated Group 1 Older Adult Care Home Residents 21,883 95% 275,964 96% Older Adult Care Home Staff 29,405 85% 381,637 88% Group 2 80+ years 251,021 83% 2,368,284 93% Health Care Worker 174,944 99% 1,139,243 100%* Group 3 75 - 79 years 177,665 90% 1,796,408 99% Group 4 70 - 74 years 252,609 90% 2,454,381 97% Clinically Extremely Vulnerable 278,967 88% 1,850,485 95% Group 5 65 - 69 years 285,768 90% 2,381,250 97% Group 6 At Risk or Carer (Under 65) 983,379 78% 6,093,082 88% Younger Adult Care Home Residents 3,822 92% 30,321 93% Group 7 60 - 64 years 373,327 92% 2,748,412 98% Group 8 55 - 59 years 465,276 91% 3,152,412 97% Group 9 50 - 54 years 510,132 90% 3,141,219 95% Data as at 15 September 2021 for age based groups and as at 12 September 2021 for non-age based groups * The number who have received their first dose exceeds the latest official estimate of the population for this group There is considerable uncertainty in the population denominators used to calculate the percentage vaccinated. Comparing implied vaccination rates for multiple sources of denominators provides some indication of uncertainty in the true values. Confidence is higher where the results from multiple sources agree more closely. Because the denominator sources are not fully independent of one another, users should interpret the range of values across sources as indicating the minimum range of uncertainty in the true value. The following datasets can be used to estimate vaccine uptake by age group for London:

ONS 2020 mid-year estimates (MYE). This is the population estimate used for age groups throughout the rest of the analysis.


Number of people ages 18 and over on the National Immunisation Management Service (NIMS)


ONS Public Health Data Asset (PHDA) dataset. This is a linked dataset combining the 2011 Census, the General Practice Extraction Service (GPES) data for pandemic planning and research and the Hospital Episode Statistics (HES). This data covers a subset of the population.

Vaccine roll out in London by Ethnic Group Understanding how vaccine uptake varies across different ethnic groups in London is complicated by two issues:

Ethnicity information for recipients is unavailable for a very large number of the vaccinations that have been delivered. As a result, estimates of vaccine uptake by ethnic group are highly sensitive to the assumptions about and treatment of the Unknown group in calculations of rates.

For vaccinations given to people aged 50 and over in London nearly 10% do not have ethnicity information available,

The accuracy of available population denominators by ethnic group is limited. Because ethnicity information is not captured in official estimates of births, deaths, and migration, the available population denominators typically rely on projecting forward patterns captured in the 2011 Census. Subsequent changes to these patterns, particularly with respect to international migration, leads to increasing uncertainty in the accuracy of denominators sources as we move further away from 2011.

Comparing estimated population sizes and implied vaccination rates for multiple sources of denominators provides some indication of uncertainty in the true values. Confidence is higher where the results from multiple sources agree more closely. Because the denominator sources are not fully independent of one another, users should interpret the range of values across sources as indicating the minimum range of uncertainty in the true value. The following population estimates are available by Ethnic group for London:

GLA Ethnic group population projections - 2016 as at 2021


ONS Population Denominators produced for Race Disparity Audit as at 2018


ETHPOP population projections produced by the University of Leeds as at 2020

Antibody prevalence estimates As part of the ONS Coronavirus (COVID-19) Infection Survey ONS publish a modelled estimate of the percent of the adult population testing positive for antibodies to Coronavirus by region. Antibodies can be generated by vaccination or previous infection.

Vaccine effects on cases, hospitalisations and deaths When the vaccine roll out began in December 2020 coronavirus cases, hospital admissions and deaths were rising steeply. The peak of infections came in London in early January 2021, before reducing during the national lockdown and as the vaccine roll out progressed. As the vaccine roll out began in older age groups the effect of vaccinations can be separated from the effect of national lockdown by comparing changes in cases, admissions and deaths

Final Dataset usted to estimate number of deaths directly avoided because of COVID-19 vaccination among older adults in Colombia.All the original data used to conform the final dataset of this study are of public access and can be downloaded from the official websites. The links to access them are shown below:

The dataset with all the COVID-19 confirmed cases and deaths in Colombia at an individual level is public available at the following link: https://www.datos.gov.co/widgets/gt2j-8ykr?mobile_redirect=true

A dataset with the information on deaths by epidemiological week between vaccinated and unvaccinated by age group is public available at the following link: https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/VS/ED/anexos-reportes-tecnicos-cohorte-esperanza.zip

For its part, information on vaccination coverage in Colombia by age group can be consulted at the following link:

https://www.minsalud.gov.co/salud/publica/Vacunacion/Paginas/Vacunacion-covid-19.aspx

According to Colombian legislation, for more detailed information and to have access to more specific information, researchers must send a formal request to the Ministry of Health and Social Protection.

Estimates of the risk of hospital admission for coronavirus (COVID-19) and death involving COVID-19 by vaccination status, overall and by age group, using anonymised linked data from Census 2021. Experimental Statistics.

Outcome definitions

For this analysis, we define a death as involving COVID-19 if either of the ICD-10 codes U07.1 (COVID-19, virus identified) or U07.2 (COVID-19, virus not identified) is mentioned on the death certificate. Information on cause of death coding is available in the User Guide to Mortality Statistics. We use date of occurrance rather than date of registration to give the date of the death.

We define COVID-109 hospitalisation as an inpatient episode in Hospital Episode Statistics where the primary diagnosis was COVID-19, identified by the ICD-19 codes (COVID-19, virus identified) or U07.2 (COVID-19, virus not identified). Where an individual had experienced more than one COVID-19 hospitalisation, the earliest that occurred within the study period was used. We define the date of COVID-19 hospitalisation as the start of the hospital episode.

ICD-10 code

U07.1 :

COVID-19, virus identified

U07.2:

COVID-19, virus not identified

Vaccination status is defined by the dose and the time since the last dose received

Unvaccinated:

no vaccination to less than 21 days post first dose

First dose 21 days to 3 months:

more than or equal to 21 days post second dose to earliest of less than 91 days post first dose or less than 21 days post second dose

First dose 3+ months:

more than or equal to 91 days post first dose to less than 21 days post second dose

Second dose 21 days to 3 months:

more than or equal to 21 days post second dose to earliest of less than 91 days post second dose or less than 21 days post third dose

Second dose 3-6 months:

more than or equal to 91 days post second dose to earliest of less than 182 days post second dose or less than 21 days post third dose

Second dose 6+ months:

more than or equal to 182 days post second dose to less than 21 days post third dose

Third dose 21 days to 3 months:

more than or equal to 21 days post third dose to less than 91 days post third dose

Third dose 3+ months:

more than or equal to 91 days post third dose

Model adjustments

Three sets of model adjustments were used

Age adjusted:

age (as a natural spline)

Age, socio-demographics adjusted:

age (as a natural spline), plus socio-demographic characteristics (sex, region, ethnicity, religion, IMD decile, NSSEC category, highest qualification, English language proficiency, key worker status)

Fully adjusted:

age (as a natural spline), plus socio-demographic characteristics (sex, region, ethnicity, religion, IMD decile, NSSEC category, highest qualification, English language proficiency, key worker status), plus health-related characteristics (disability, self-reported health, care home residency, number of QCovid comorbidities (grouped), BMI category, frailty flag and hospitalisation within the last 21 days.

Age

Age in years is defined on the Census day 2021 (21 March 2021). Age is included in the model as a natural spline with boundary knots at the 10th and 90th centiles and internal knots at the 25th, 50th and 75th centiles. The positions of the knots are calculated separately for the overall model and for each age group for the stratified model.

This dataset merges three distinct data sources to explore the relationship between COVID-19 death rates, vaccination efforts, and public sentiment on Twitter from December 25, 2020 to March 29, 2022. It includes 2,000 cleaned rows with 16 variables, created by combining global health statistics and social media sentiment data.

Sources & Variables:

1. COVID-19 Deaths Data (scraped from Worldometer - COVID-19 Deaths via BeautifulSoup):

   • Date: Date of record
   • daily_increase_percent: % change in deaths from previous day
   • Season: Derived from date (Winter, Spring, Summer, Fall)

2. Tweet Sentiment Data : COVID Vaccine Tweets Dataset

   • Date: Tweet timestamp
   • text_sentiment: Sentiment label (positive, neutral, negative) from NLTK’s SentimentIntensityAnalyzer
   • user_verified: Whether the user is verified
   • user_since_days: Age of the Twitter account (in days)
   • country: Cleaned user location

3. Vaccination Data : Vaccination Dataset

   • Date: Date of record
   • total_vaccinations_per_hundred: Doses per 100 people
   • daily_vaccinations: Daily dose count
   • vaccine_group: Grouped vaccine type (e.g., mRNA, Viral Vector)
   • country: Country name

Preprocessing Summary:

• Merged by Date and country
• Cleaned invalid country names (e.g., “moon”, “nowhere”)
• Standardized all datetime formats
• Removed entries with missing or unreliable values
• Created derived variables: Season, user_since_days, vaccine_group

This dataset was used in a final data science project to:

• Classify public sentiment toward vaccines using health indicators
• Predict daily COVID-19 death counts using sentiment and vaccination data