Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses.

On 6/16/2023 CDPH replaced the booster measures with a new “Up to Date” measure based on CDC’s new recommendations, replacing the primary series, boosted, and bivalent booster metrics The definition of “primary series complete” has not changed and is based on previous recommendations that CDC has since simplified. A person cannot complete their primary series with a single dose of an updated vaccine. Whereas the booster measures were calculated using the eligible population as the denominator, the new up to date measure uses the total estimated population. Please note that the rates for some groups may change since the up to date measure is calculated differently than the previous booster and bivalent measures.

This data is from the same source as the Vaccine Progress Dashboard at https://covid19.ca.gov/vaccination-progress-data/ which summarizes vaccination data at the county level by county of residence. Where county of residence was not reported in a vaccination record, the county of provider that vaccinated the resident is included. This applies to less than 1% of vaccination records. The sum of county-level vaccinations does not equal statewide total vaccinations due to out-of-state residents vaccinated in California.

These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons.

Totals for the Vaccine Progress Dashboard and this dataset may not match, as the Dashboard totals doses by Report Date and this dataset totals doses by Administration Date. Dose numbers may also change for a particular Administration Date as data is updated.

Previous updates:

• On March 3, 2023, with the release of HPI 3.0 in 2022, the previous equity scores have been updated to reflect more recent community survey information. This change represents an improvement to the way CDPH monitors health equity by using the latest and most accurate community data available. The HPI uses a collection of data sources and indicators to calculate a measure of community conditions ranging from the most to the least healthy based on economic, housing, and environmental measures.

• Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 16+ and age 5+ denominators have been uploaded as archived tables.

• Starting on May 29, 2021 the methodology for calculating on-hand inventory in the shipped/delivered/on-hand dataset has changed. Please see the accompanying data dictionary for details. In addition, this dataset is now down to the ZIP code level.

This is one of three datasets related to the Prevention Agenda Tracking Indicators county level data posted on this site. Each dataset consists of county level data for 68 health tracking indicators and sub-indicators for the Prevention Agenda 2013-2017: New York State’s Health Improvement Plan. A health tracking indicator is a metric through which progress on a certain area of health improvement can be assessed. The indicators are organized by the Priority Area of the Prevention Agenda as well as the Focus Area under each Priority Area. Each dataset includes tracking indicators for the five Priority Areas of the Prevention Agenda 2013-2017. The latest data dataset includes the most recent county level data for all indicators. The trend dataset includes the most recent county level data and historical data, where available. Each dataset also includes the Prevention Agenda 2017 state targets for the indicators. Sub-indicators are included in these datasets to measure health disparities among socioeconomic groups. For more information, check out: http://www.health.ny.gov/prevention/prevention_agenda/2013-2017/ and https://www.health.ny.gov/PreventionAgendaDashboard, or go to the “About” tab.

Worldwide Measles Vaccination Rates

Measles Vaccination Rates by Country and Year

By Throwback Thursday [source]

About this dataset

The WHO Region Name column represents the name of the World Health Organization (WHO) region to which each country belongs. The ISO Code column contains the standardized ISO code for each country. The Country Name column specifies the name of each individual country included in this dataset.

The Percentage of Surviving Infants receiving Dose column indicates the percentage of infants who received a specific dose of the measles vaccine and survived. This serves as an important indicator for tracking vaccination rates and overall healthcare effectiveness in relation to preventing measles deaths among infants.

Overall, this dataset provides valuable insights into global measles vaccination rates over a span of several decades. By analyzing this information, researchers and policymakers can assess trends in immunization coverage, identify areas where vaccine uptake is low or improving over time, and guide targeted interventions to increase vaccination rates and reduce infant mortality due to measles infection

How to use the dataset

Introduction:

• Understanding the Columns: a. WHO Region Name: It represents the name of the World Health Organization (WHO) region to which a country belongs. b. ISO Code: It provides the ISO code of each country, which is a standardized three-letter code assigned to represent countries. c. Country Name: This column contains the name of each country involved in the dataset. d. Vaccine: It indicates the type of vaccine administered for measles. e. Year: The year when data was recorded, ranging from 1980 to 2017 (numeric). f. Percentage of Surviving Infants receiving Dose: This represents the percentage value denoting infants who received a specified dose of measles vaccine and survived.

• Navigating through Data:

• To explore data for specific countries or regions, filter by using either 'Country Name' or 'WHO Region Name'.

• Utilize filtering according to specific vaccines if you are interested in studying particular types.

• Selection Tools:

• Use pandas library in Python or similar tools/software platforms like Excel or Google Sheets that support filtering capabilities based on columns mentioned above.

• Employ functions such as dataframe.loc[] in Python's pandas library for extracting desired subsets based on specific filters.

• Data Analysis Ideas: Here are some potential analysis ideas using this dataset:

a) Analyzing Trends Over Time: - Generate line plots/graphs comparing vaccination rates across multiple countries/regions over different years to identify trends and patterns. - Categorize countries/regions by their WHO regions utilizing bar plots/graphs, and analyze how vaccination rates vary within each region over time.

b) Regional Comparisons: - Compare the measles vaccination rates between countries within and across different WHO regions. - Identify the top-performing countries in terms of measles vaccination rates for specific years, regions, or vaccine types.

c) Impact of Vaccine Types: - Assess the impact of different measles vaccines by comparing their adoption rates and effectiveness. - Analyze how the percentage of surviving infants receiving a particular vaccine dose varies over time for individual countries or regions.

d) Outlier Detection: - Explore if there are any significant variations or outliers in measles vaccination rates among different countries or regions. Investigate possible

Research Ideas

• Evaluating the effectiveness of measles vaccination programs: This dataset can be used to analyze the percentage of infants who received the measles vaccine and survived in different countries and regions over time. By comparing this data with information on measles cases and mortality rates, researchers can assess the effectiveness of vaccination programs in preventing measles outbreaks and reducing infant mortality.
• Identifying disparities in vaccine coverage: The dataset can also be used to identify disparities in measles vaccine coverage between countries and regions. By examining the percentage of infants receiving the vaccine across different geographical areas, researchers can identify areas with low coverage rates and target interventions to improve vaccination rates in those regions.
• Assessing trends over time: Since this dataset includes data from 1980 to 2017, it allows for analysis of trends in measles vaccination rates over several decades. Researchers can examine whether there have been improvement...

After October 13, 2022, this dataset will no longer be updated as the related CDC COVID Data Tracker site was retired on October 13, 2022.

This dataset contains historical trends in vaccinations and cases by age group, at the US national level. Data is stratified by at least one dose and fully vaccinated. Data also represents all vaccine partners including jurisdictional partner clinics, retail pharmacies, long-term care facilities, dialysis centers, Federal Emergency Management Agency and Health Resources and Services Administration partner sites, and federal entity facilities.

State-by-state data on United States COVID-19 vaccinations data

Acknowledgement and License

All data are produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited. In the case of our vaccination dataset, please give the following citation:

Mathieu, E., Ritchie, H., Ortiz-Ospina, E. et al. A global database of COVID-19 vaccinations. Nat Hum Behav (2021). https://doi.org/10.1038/s41562-021-01122-8

Data Set Column Details

location : name of the state or federal entity. date: date of the observation. total vaccinations: total number of doses administered. This is counted as a single dose, and may not equal the total number of people vaccinated, depending on the specific dose regime (e.g. people receive multiple doses). If a person receives one dose of the vaccine, this metric goes up by 1. If they receive a second dose, it goes up by 1 again. total vaccinations per hundred: total vaccinations per 100 people in the total population of the state. daily vaccinations raw: daily change in the total number of doses administered. It is only calculated for consecutive days. This is a raw measure provided for data checks and transparency, but we strongly recommend that any analysis on daily vaccination rates be conducted using daily vaccinations instead. daily vaccinations: new doses administered per day (7-day smoothed). For countries that don't report data on a daily basis, we assume that doses 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. An example of how we perform this calculation can be found here. daily vaccinations per million: daily vaccinations per 1,000,000 people in the total population of the state. people vaccinated: total number of people who received at least one vaccine dose. If a person receives the first dose of a 2-dose vaccine, this metric goes up by 1. If they receive the second dose, the metric stays the same. people vaccinated per hundred: people vaccinated per 100 people in the total population of the state. people fully vaccinated: total number of people who received all doses prescribed by the initial vaccination protocol. If a person receives the first dose of a 2-dose vaccine, this metric stays the same. If they receive the second dose, the metric goes up by 1. people fully vaccinated per hundred: people fully vaccinated per 100 people in the total population of the state. total distributed: cumulative counts of COVID-19 vaccine doses recorded as shipped in CDC's Vaccine Tracking System. total distributed per hundred: cumulative counts of COVID-19 vaccine doses recorded as shipped in CDC's Vaccine Tracking System per 100 people in the total population of the state. share doses used: share of vaccination doses administered among those recorded as shipped in CDC's Vaccine Tracking System. total boosters: total number of COVID-19 vaccination booster doses administered (doses administered beyond the number prescribed by the initial vaccination protocol) total boosters per hundred: total boosters per 100 people in the total population.

Time Span

20th Dec 2020 to 28th Dec 2022

Worldwide Measles Vaccinations

Tracking measles vaccination rates worldwide

By Throwback Thursday [source]

About this dataset

The dataset contains several columns that help analyze and compare the vaccination rates across different regions. These columns include: - Country: The name of the country where the data was collected. - ISO Code: The three-letter code assigned to each country by the International Organization for Standardization (ISO). - WHO Region: The region to which a particular country belongs as defined by the World Health Organization (WHO). - Data Source: The source from where the data was obtained, ensuring transparency in reporting. - Year: The year in which the measles vaccine coverage was recorded. - Immunization Coverage (%): This column represents the percentage of individuals vaccinated against measles within a given year for each respective country.

By analyzing this dataset, researchers and policymakers can gain useful insights into global immunization efforts, identify geographical disparities in vaccine coverage rates, assess the impact of vaccination campaigns over time, measure progress towards eliminating measles as per international goals, and inform evidence-based decision-making for improving public health outcomes worldwide.

Please note that this dataset does not contain any dates specific to individual records

How to use the dataset

• Understanding the Columns Let's begin by understanding the columns present in this dataset:

  • Country - Represents the name of a specific country or region.
  • Year - Indicates the year for which vaccination data is available.
  • Vaccination Rate- Represents the percentage of individuals vaccinated against measles in a particular country or region during a given year.

• Exploratory Data Analysis The first step when working with any new dataset is conducting exploratory data analysis (EDA) to gain insights into its contents and structure. Here are some key EDA steps you can take:

  • Identify unique countries/regions present in the Country column.
  • Determine which years have data available in this dataset.
  • Calculate summary statistics such as mean, median, minimum, maximum vaccination rates.

• Comparative Analysis One interesting aspect of this dataset is its ability to compare measles vaccination rates across different countries and regions over time. Here's how you can perform comparative analysis:

  i) Select specific countries/regions from the Country column that you want to analyze.

  ii) Filter out these selected countries/regions from your dataframe for further analysis.

  iii) Plot line charts or bar graphs to compare their vaccination rates over years.

• Analyzing Trends and Patterns By analyzing trends and patterns within this dataset, one can gain valuable insights into global measles vaccination behavior and effectiveness of immunization programs. Here are a few ideas to get started:

  i) Plot line and bar graphs to visualize overall trends in measles vaccination rates worldwide.

  ii) Identify countries where vaccination rates have significantly increased or decreased over time.

  iii) Identify any patterns or relationships between vaccination rates and other factors such as GDP, population, etc.

• Identifying Outliers While analyzing this dataset, pay attention to possible outliers that may skew your analysis or predictions. By identifying and handling these outliers appropriately, you can ensure robust conclusions from your analysis.

• Data Visualization Utilize data visualization techniques such as

Research Ideas

• Identifying countries with low measles vaccination rates: By analyzing the dataset, one can identify countries or regions with low measles vaccination rates over time. This information can be used to target and prioritize interventions, education campaigns, and resources to increase vaccination coverage in these areas.
• Understanding the relationship between vaccination rates and measles outbreaks: The dataset can help analyze the correlation between measles vaccination rates and outbreaks of this infectious disease worldwide. Researchers can investigate how higher vaccine coverage is associated with lower incidence of measles cases, highlighting the importance of immunization for disease prevention.
• Evaluating the impact of immunization programs: This dataset can be used to assess the effectiveness of different immunization programs implemented by various countries or...

**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.

CDC COVID-19 Vaccine Tracker

Cumulative and Daily Counts of COVID-19 Vaccine Doses in the United States

By Nicky Forster [source]

About this dataset

The dataset contains data points such as the cumulative count of people who have received at least one dose of the vaccine, new doses administered on a specific date, cumulative count of doses distributed in the country, percentage of population that has completed the full vaccine series, cumulative count of Pfizer and Moderna vaccine doses administered in each state, seven-day rolling averages for new doses administered and distributed, among others.

It also provides insights into the vaccination status at both national and state levels. The dataset includes information on the percentage of population that has received at least one dose of the vaccine, percentage of population that has completed the full vaccine series, cumulative counts per 100k population for both distributed and administered doses.

Additionally, it presents data specific to each state, including their abbreviation and name. It outlines details such as cumulative counts per 100k population for both distributed and administered doses in each state. Furthermore, it indicates if there were instances where corrections resulted in single-day negative counts.

The dataset is compiled from daily snapshots obtained from CDC's COVID Data Tracker. Please note that there may be reporting delays by healthcare providers up to 72 hours after administering a dose.

This comprehensive dataset serves various purposes including tracking vaccination progress over time across different locations within the United States. It can be used by researchers, policymakers or anyone interested in analyzing trends related to COVID-19 vaccination efforts at both national and state levels

How to use the dataset

• Familiarize Yourself with the Columns: Take a look at the available columns in this dataset to understand what information is included. These columns provide details such as state abbreviations, state names, dates of data snapshots, cumulative counts of doses distributed and administered, people who have received at least one dose or completed the vaccine series, percentages of population coverage, manufacturer-specific data, and seven-day rolling averages.

• Explore Cumulative Counts: The dataset includes cumulative counts that show the total number of doses distributed or administered over time. You can analyze these numbers to track trends in vaccination progress in different states or regions.

• Analyze Daily Counts: The dataset also provides daily counts of new vaccine doses distributed and administered on specific dates. By examining these numbers, you can gain insights into vaccination rates on a day-to-day basis.

• Study Population Coverage Metrics: Metrics such as pct_population_received_at_least_one_dose and pct_population_series_complete give you an understanding of how much of each state's population has received at least one dose or completed their vaccine series respectively.

• Utilize Manufacturer Data: The columns related to Pfizer and Moderna provide information about the number of doses administered for each manufacturer separately. By analyzing this data, you can compare vaccination rates between different vaccines.

• Consider Rolling Averages: The seven-day rolling average columns allow you to smooth out fluctuations in daily counts by calculating an average over a week's time window. This can help identify long-term trends more accurately.

• Compare States: You can compare vaccination progress between different states by filtering the dataset based on state names or abbreviations. This way, you can observe variations in distribution and administration rates among different regions.

• Visualize the Data: Creating charts and graphs will help you visualize the data more effectively. Plotting trends over time or comparing different metrics for various states can provide powerful visual representations of vaccination progress.

• Stay Informed: Keep in mind that this dataset is continuously updated as new data becomes available. Make sure to check for any updates or refreshed datasets to obtain the most recent information on COVID-19 vaccine distributions and administrations

Research Ideas

• Vaccination Analysis: This dataset can be used to analyze the progress of COVID-19 vaccinations in the United States. By examining the cumulative counts of doses distributed and administered, as well as the number of people who have received at least one dose or completed the vaccine series, researchers and policymakers can assess how effectively vaccines are being rolled out and monitor...

NOTE: As of 2/16/2023, this page is no longer being updated. This table shows the number and percent of people that have initiated COVID-19 vaccination and are fully vaccinated by race / ethnicity and town. It includes people of all ages. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. A person who has received at least one dose of any vaccine is considered to have initiated vaccination. A person is considered fully vaccinated if they have completed a primary series by receiving 2 doses of the Pfizer, Novavax or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the number who have received at least one dose. Race and ethnicity data may be self-reported or taken from an existing electronic health care record. Reported race and ethnicity information is used to create a single race/ethnicity variable. People with Hispanic ethnicity are classified as Hispanic regardless of reported race. People with a missing ethnicity are classified as non-Hispanic. People with more than one race are classified as multiple race. A vaccine coverage percentage cannot be calculated for people classified as NH Other race or NH Unknown race since there are not population size estimates for these groups. Data quality assurance activities suggest that NH Other may represent a missing value. Vaccine coverage estimates in specific race/ethnicity groups may be underestimated as result of the exclusion of records classified as NH Unknown Race or NH Other Race. Town of residence is verified by geocoding the reported address and then mapping it a town using municipal boundaries. If an address cannot be geocoded, the reported town is used. Town-level coverage estimates have been capped at 100%. Observed coverage may be greater than 100% for multiple reasons, including census denominator data not including all individuals that currently reside in the town (e.g., part time residents, change in population size since the census) or potential data reporting errors. The population denominators for these town- and age-specific coverage estimates are based on 2014 census estimates. This is the most recent year for which reliable town- and age-specific estimates are available. (https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Town-Population-with-Demographics). Changes in the size and composition of the population between 2014 and 2021 may results in inaccuracy in vaccine coverage estimates. For example, the size of the Hispanic population may be underestimated in a town given the reported increase in the size of the Hispanic population between the 2010 and 2020 censuses resulting in inflated vaccine coverage estimates. The 2014 census data are grouped in 5-year age bands. For vaccine coverage age groupings not consistent with a standard 5-year age band, each age was assumed to be 20% of the total within a 5-year age band. However, given the large deviation from this assumption for Mansfield because of the presence of the University of Connecticut, the age distribution observed in the 2010 census for the age bands 15 to 19 and 20 to 24 was used to estimate the population denominators. This table does not included doses administered to CT residents by out-of-state providers or by some Federal entities (including Department of Defense, Department of Correction, Department of Veteran’s Affairs, Indian Health Service) because they are not yet reported to CT WiZ (the CT immunization Information System). It is expected that these data will be added in the future. Caution should be used when interpreting coverage estimates for towns with large college/university populations since coverage may be underestimated. In the census, college/university students who live on or just off campus would be counted in the college/university town. However, if a student was vaccinated while study

Note: This dataset is no longer being updated due to the end of the COVID-19 Public Health Emergency.

The California Department of Public Health (CDPH) is identifying vaccination status of COVID-19 cases, hospitalizations, and deaths by analyzing the state immunization registry and registry of confirmed COVID-19 cases. Post-vaccination cases are individuals who have a positive SARS-Cov-2 molecular test (e.g. PCR) at least 14 days after they have completed their primary vaccination series.

Tracking cases of COVID-19 that occur after vaccination is important for monitoring the impact of immunization campaigns. While COVID-19 vaccines are safe and effective, some cases are still expected in persons who have been vaccinated, as no vaccine is 100% effective. For more information, please see https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/Post-Vaccine-COVID19-Cases.aspx

Post-vaccination infection data is updated monthly and includes data on cases, hospitalizations, and deaths among the unvaccinated and the vaccinated. Partially vaccinated individuals are excluded. To account for reporting and processing delays, there is at least a one-month lag in provided data (for example data published on 9/9/22 will include data through 7/31/22).

Notes:

• On September 9, 2022, the post-vaccination data has been changed to compare unvaccinated with those with at least a primary series completed for persons age 5+. These data will be updated monthly (first Thursday of the month) and include at least a one month lag.

• On February 2, 2022, the post-vaccination data has been changed to distinguish between vaccination with a primary series only versus vaccinated and boosted. The previous dataset has been uploaded as an archived table. Additionally, the lag on this data has been extended to 14 days.

• On November 29, 2021, the denominator for calculating vaccine coverage has been changed from age 16+ to age 12+ to reflect new vaccine eligibility criteria. The previous dataset based on age 16+ denominators has been uploaded as an archived table.

IntroductionIn relatively wealthy countries, substantial between-country variability in COVID-19 vaccination coverage occurred. We aimed to identify influential national-level determinants of COVID-19 vaccine uptake at different COVID-19 pandemic stages in such countries.MethodsWe considered over 50 macro-level demographic, healthcare resource, disease burden, political, socio-economic, labor, cultural, life-style indicators as explanatory factors and coverage with at least one dose by June 2021, completed initial vaccination protocols by December 2021, and booster doses by June 2022 as outcomes. Overall, we included 61 European or Organisation for Economic Co-operation and Development (OECD) countries. We performed 100 multiple imputations correcting for missing data and partial least squares regression for each imputed dataset. Regression estimates for the original covariates were pooled over the 100 results obtained for each outcome. Specific analyses focusing only on European Union (EU) or OECD countries were also conducted.ResultsHigher stringency of countermeasures, and proportionately more older adults, female and urban area residents, were each strongly and consistently associated with higher vaccination rates. Surprisingly, socio-economic indicators such as gross domestic product (GDP), democracy, and education had limited explanatory power. Overall and in the OECD, greater perceived corruption related strongly to lower vaccine uptake. In the OECD, social media played a noticeable positive role. In the EU, right-wing government ideology exhibited a consistently negative association, while cultural differences had strong overall influence.ConclusionRelationships between country-level factors and COVID-19 vaccination uptake depended on immunization stage and country reference group. Important determinants include stringency, population age, gender and urbanization, corruption, government ideology and cultural context.

NOTE: This dataset has been retired and marked as historical-only. The recommended dataset to use in its place is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccination-Coverage-Region-HCEZ-/5sc6-ey97.

COVID-19 vaccinations administered to Chicago residents by Healthy Chicago Equity Zones (HCEZ) based on the reported address, race-ethnicity, and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE).

Healthy Chicago Equity Zones is an initiative of the Chicago Department of Public Health to organize and support hyperlocal, community-led efforts that promote health and racial equity. Chicago is divided into six HCEZs. Combinations of Chicago’s 77 community areas make up each HCEZ, based on geography. For more information about HCEZs including which community areas are in each zone see: https://data.cityofchicago.org/Health-Human-Services/Healthy-Chicago-Equity-Zones/nk2j-663f

Vaccination Status Definitions:

·People with at least one vaccine dose: Number of people who have received at least one dose of any COVID-19 vaccine, including the single-dose Johnson & Johnson COVID-19 vaccine.

·People with a completed vaccine series: Number of people who have completed a primary COVID-19 vaccine series. Requirements vary depending on age and type of primary vaccine series received.

·People with a bivalent dose: Number of people who received a bivalent (updated) dose of vaccine. Updated, bivalent doses became available in Fall 2022 and were created with the original strain of COVID-19 and newer Omicron variant strains.

Weekly cumulative totals by vaccination status are shown for each combination of race-ethnicity and age group within an HCEZ. Note that each HCEZ has a row where HCEZ is “Citywide” and each HCEZ has a row where age is "All" so care should be taken when summing rows.

Vaccinations are counted based on the date on which they were administered. Weekly cumulative totals are reported from the week ending Saturday, December 19, 2020 onward (after December 15, when vaccines were first administered in Chicago) through the Saturday prior to the dataset being updated.

Population counts are from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-year estimates.

Coverage percentages are calculated based on the cumulative number of people in each population subgroup (age group by race-ethnicity within an HCEZ) who have each vaccination status as of the date, divided by the estimated number of people in that subgroup.

Actual counts may exceed population estimates and lead to >100% coverage, especially in small race-ethnicity subgroups of each age group within an HCEZ. All coverage percentages are capped at 99%.

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. At any given time, this dataset reflects data currently known to CDPH.

Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined.

CDPH uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact its estimates. Data reported in I-CARE only includes doses administered in Illinois and some doses administered outside of Illinois reported historically by Illinois providers. Doses administered by the federal Bureau of Prisons and Department of Defense are also not currently reported in I-CARE. The Veterans Health Administration began reporting doses in I-CARE beginning September 2022. Due to people receiving vaccinations that are not recorded in I-CARE that can be linked to their record, such as someone receiving a vaccine dose in another state, the number of people with a completed series or a booster dose is underesti

NOTE: This dataset replaces two previous ones. Please see below. Chicago residents who are up to date with COVID-19 vaccines, based on the reported address, race-ethnicity, sex, and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). “Up to date” refers to individuals who meet the CDC’s updated COVID-19 vaccination criteria based on their age and prior vaccination history. For surveillance purposes, up to date is defined based on the following criteria: People ages 5 years and older: · Are up to date when they receive 1+ doses of a COVID-19 vaccine during the current season. Children ages 6 months to 4 years: · Children who have received at least two prior COVID-19 vaccine doses are up to date when they receive one additional dose of COVID-19 vaccine during the current season, regardless of vaccine product. · Children who have received only one prior COVID-19 vaccine dose are up to date when they receive one additional dose of the current season's Moderna COVID-19 vaccine or two additional doses of the current season's Pfizer-BioNTech COVID-19 vaccine. · Children who have never received a COVID-19 vaccination are up to date when they receive either two doses of the current season's Moderna vaccine or three doses of the current season's Pfizer-BioNTech vaccine. This dataset takes the place of two previous datasets, which cover doses administered from December 15, 2020 through September 13, 2023 and are marked has historical: - https://data.cityofchicago.org/Health-Human-Services/COVID-19-Daily-Vaccinations-Chicago-Residents/2vhs-cf6b - https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccinations-by-Age-and-Race-Ethnicity/37ac-bbe3. Data Notes: Weekly cumulative totals of people up to date are shown for each combination of race-ethnicity, sex, and age group. Note that race-ethnicity, age, and sex all have an option for “All” so care should be taken when summing rows. Coverage percentages are calculated based on the cumulative number of people in each race-ethnicity/age/sex population subgroup who are considered up to date as of the week ending date divided by the estimated number of people in that subgroup. Population counts are obtained from the 2020 U.S. Decennial Census. Actual counts may exceed population estimates and lead to coverage estimates that are greater than 100%, especially in smaller demographic groupings with smaller populations. Additionally, the medical provider may report incorrect demographic information for the person receiving the vaccination, which may lead to over- or underestimation of vaccination coverage. All coverage percentages are capped at 99%. Weekly cumulative counts and coverage percentages are reported from the week ending Saturday, September 16, 2023 onward through the Saturday prior to the dataset being updated. 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. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. The Chicago Department of Public Health uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact our estimates. Individuals may receive vaccinations that are not recorded in the Illinois immunization registry, I-CARE, such as those administered in another state, causing underestimation of the number individuals who are up to date. Inconsistencies in records of separate doses administered to the same person, such as slight variations in dates of birth, can result in duplicate records for a person and underestimate the number of people who are up to date.

Vaccination rates and odds ratios by socio-demographic group among people living in England.

NOTE: As of 2/16/2023 this table is no longer being updated. For information on COVID-19 Updated (Bivalent) Booster Coverage, go to https://data.ct.gov/Health-and-Human-Services/COVID-19-Updated-Bivalent-Booster-Coverage-By-Race/8267-bg4w.

Important change as of June 1, 2022

As of June 1, 2022, we will be using 2020 DPH provisional census estimates* to calculate vaccine coverage percentages by age at the state level. 2020 estimates will replace the 2019 estimates that have been used. Caution should be taken when making comparisons of percentages calculated using the 2019 and 2020 census estimates since observed difference may result from the shift in the denominator. The age groups in the state-level data tables will also be changing as a result of the switch to the new denominator.

• DPH Provisional State and County Characteristics Estimates April 1, 2020. Hayes L, Abdellatif E, Jiang Y, Backus K (2022) Connecticut DPH Provisional April 1, 2020 State Population Estimates by 18 age groups, sex, and 6 combined race and ethnicity groups. Connecticut Department of Public Health, Health Statistics & Surveillance, SAR, Hartford, CT.

This table shows the number and percent of people that have initiated COVID-19 vaccination, are fully vaccinated and had additional dose 1 by race / ethnicity and age group.

All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. The age groups in the state-level data tables will also be changing as a result of the switch to the new denominator.

Population size estimates are based on 2019 DPH census estimates until 5/26/2022. From 6/1/2022, 2020 DPH provisional census estimates are used.

In the data shown here, a person who has received at least one dose of COVID-19 vaccine is considered to have initiated vaccination. A person is considered fully vaccinated if he/she has completed a primary vaccination series by receiving 2 doses of the Pfizer, Novavax or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the people who have received at least one dose.

A person who completed a Pfizer, Moderna, Novavax or Johnson & Johnson primary series (as defined above) and then had an additional monovalent dose of COVID-19 vaccine is considered to have had additional dose 1. The additional dose may be Pfizer, Moderna, Novavax or Johnson & Johnson and may be a different type from the primary series. For people who had a primary Pfizer or Moderna series, additional dose 1 was counted starting August 18th, 2021. For people with a Johnson & Johnson primary series additional dose 1 was counted starting October 22nd, 2021. For most people, additional dose 1 is a booster. However, additional dose 1 may represent a supplement to the primary series for a people who is moderately or severely immunosuppressed. Bivalent booster administrations are not included in the additional dose 1 calculations.

The percent with at least one dose many be over-estimated, and the percent fully vaccinated and with additional dose 1 may be under-estimated because of vaccine administration records for individuals that cannot be linked because of differences in how names or date of birth are reported.

Race and ethnicity data may be self-reported or taken from an existing electronic health care record. Reported race and ethnicity information is used to create a single race/ethnicity variable. People with Hispanic ethnicity are classified as Hispanic regardless of reported race. People with a missing ethnicity are classified as non-Hispanic. People with more than one race are classified as multiple races.

A vaccine coverage percentage cannot be calculated for people classified as NH Other race or NH Unknown race since there are not population size estimates for these groups. Data quality assurance activities suggest that in at least some cases NH Other may represent a missing value. Vaccine coverage estimates in specific race/ethnicity groups may be underestimated as result of the classification of records as NH Unknown Race or NH Other Race.

Connecticut COVID-19 Vaccine Program providers are required to report information on all COVID-19 vaccine doses administered to CT WiZ, the Connecticut Immunization Information System. This includes doses given to residents of CT and to residents of other states vaccinated in CT. Data on doses administered to CT residents out-of-state are being added to CT WiZ jurisdiction-by-jurisdiction. Doses administered by some Federal entities (including Department of Defense, Department of Correction, Department of Veteran’s Affairs, Indian Health Service) are not yet reported to CT WiZ. Data reported here reflect the vaccination records reported to CT WiZ. However, once CT residents who have received doses in each jurisdiction are added to CT WiZ, the records for residents of that jurisdiction vaccinated in CT are removed. For example, when CT residents vaccinated in NYC were added, NYC residents vaccinated in CT were removed.

Note: This dataset takes the place of the original "COVID-19 Vaccinations by Race/Ethnicity" dataset (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Race-Ethnicity/xkga-ifz3 ), which will not be updated after 5/20/2021 and “COVID-19 Vaccinations by Race / Ethnicity” dataset (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Race-Ethnicity/ybkg-w5x2), which will not be updated after 10/20/2021.

BackgroundThe global COVID-19 pandemic is still ongoing, and cross-country and cross-period variation in COVID-19 age-adjusted case fatality rates (CFRs) has not been clarified. Here, we aimed to identify the country-specific effects of booster vaccination and other features that may affect heterogeneity in age-adjusted CFRs with a worldwide scope, and to predict the benefit of increasing booster vaccination rate on future CFR.MethodCross-temporal and cross-country variations in CFR were identified in 32 countries using the latest available database, with multi-feature (vaccination coverage, demographic characteristics, disease burden, behavioral risks, environmental risks, health services and trust) using Extreme Gradient Boosting (XGBoost) algorithm and SHapley Additive exPlanations (SHAP). After that, country-specific risk features that affect age-adjusted CFRs were identified. The benefit of booster on age-adjusted CFR was simulated by increasing booster vaccination by 1–30% in each country.ResultsOverall COVID-19 age-adjusted CFRs across 32 countries ranged from 110 deaths per 100,000 cases to 5,112 deaths per 100,000 cases from February 4, 2020 to Jan 31, 2022, which were divided into countries with age-adjusted CFRs higher than the crude CFRs and countries with age-adjusted CFRs lower than the crude CFRs (n = 9 and n = 23) when compared with the crude CFR. The effect of booster vaccination on age-adjusted CFRs becomes more important from Alpha to Omicron period (importance scores: 0.03–0.23). The Omicron period model showed that the key risk factors for countries with higher age-adjusted CFR than crude CFR are low GDP per capita and low booster vaccination rates, while the key risk factors for countries with higher age-adjusted CFR than crude CFR were high dietary risks and low physical activity. Increasing booster vaccination rates by 7% would reduce CFRs in all countries with age-adjusted CFRs higher than the crude CFRs.ConclusionBooster vaccination still plays an important role in reducing age-adjusted CFRs, while there are multidimensional concurrent risk factors and precise joint intervention strategies and preparations based on country-specific risks are also essential.

Context

The data is collected from OWID (Our World in Data) GitHub repository, which is updated on daily bases.

Content

This dataset contains only one file vaccinations.csv, which contains the records of vaccination doses received by people from all the countries. * location: name of the country (or region within a country). * iso_code: ISO 3166-1 alpha-3 – three-letter country codes. * date: date of the observation. * total_vaccinations: total number of doses administered. This is counted as a single dose, and may not equal the total number of people vaccinated, depending on the specific dose regime (e.g. people receive multiple doses). If a person receives one dose of the vaccine, this metric goes up by 1. If they receive a second dose, it goes up by 1 again. * total_vaccinations_per_hundred: total_vaccinations per 100 people in the total population of the country. * daily_vaccinations_raw: daily change in the total number of doses administered. It is only calculated for consecutive days. This is a raw measure provided for data checks and transparency, but we strongly recommend that any analysis on daily vaccination rates be conducted using daily_vaccinations instead. * daily_vaccinations: new doses administered per day (7-day smoothed). For countries that don't report data on a daily basis, we assume that doses 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. An example of how we perform this calculation can be found here. * daily_vaccinations_per_million: daily_vaccinations per 1,000,000 people in the total population of the country. * people_vaccinated: total number of people who received at least one vaccine dose. If a person receives the first dose of a 2-dose vaccine, this metric goes up by 1. If they receive the second dose, the metric stays the same. * people_vaccinated_per_hundred: people_vaccinated per 100 people in the total population of the country. * people_fully_vaccinated: total number of people who received all doses prescribed by the vaccination protocol. If a person receives the first dose of a 2-dose vaccine, this metric stays the same. If they receive the second dose, the metric goes up by 1. * people_fully_vaccinated_per_hundred: people_fully_vaccinated per 100 people in the total population of the country.

Note: for people_vaccinated and people_fully_vaccinated we are dependent on the necessary data being made available, so we may not be able to make these metrics available for some countries.

Acknowledgements

This data collected by Our World in Data which gets updated daily on their Github.

Inspiration

Possible uses for this dataset could include: - Sentiment analysis in a variety of forms - Statistical analysis over time.

Note: As of 1/13/24, this dataset is no longer updated. This dataset reports daily on the number of people vaccinated by New York providers who have received a dose of the updated COVID-19 vaccine authorized on September 12, 2023. New York providers include hospitals, pharmacies, and other providers registered with the State to serve as points of distribution.

This dataset is created by the New York State Department of Health from data reported to the New York State Immunization Information System (NYSIIS). County-level vaccination data is based on data reported to NYSIIS by the providers administering vaccines. Residency is self-reported by the individual being vaccinated. This dataset includes limited data on vaccines administered through Federal entities (Veterans Health Administration) or performed outside of New York State to New York residents and will not be reflective of every dose administered to New York State residents in those settings. It does not include residents of New York City. NYSIIS data is used for county-level statistics.

These data represent a lower-bound estimate on updated COVID-19 vaccination totals. With the end of the COVID-19 public health emergency, COVID-19 vaccination records are no longer required to be submitted to NYSIIS for adults 19+. Reporting remains mandatory for children 18 and under.

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

BackgroundCountries deliver vaccines either through routine health services or supplementary immunization activities (SIAs), usually community-based or door-to-door immunization campaigns. While SIAs have been successful at increasing coverage of vaccines in low- and middle-income countries, they may disrupt the delivery of routine health services. We examine the impact of SIAs on routine vaccine coverage in five low-income countries.MethodsData on the number and timing of SIAs conducted in various countries was compiled by WHO and obtained through UNICEF. Information on the coverage of vaccines not targeted by SIAs (e.g., DPT) was extracted from the Demographic and Health Surveys. We focus on SIAs that took place between 1996 and 2013 in Bangladesh, Senegal, Togo, Gambia, and Cote d’Ivoire, and examine outcomes for children aged 12–59 months. To avoid biases resulting from non-random placement and timing of SIAs, we use age of a child at her first SIA as an instrumental variable for total exposure to SIAs.ResultsWe find that SIA exposure reduced the likelihood of receiving routine vaccines in all the countries included in the study; the coefficients of interest are however statistically insignificant for Gambia and Cote d’Ivoire. In countries that witnessed statistically significant SIA-induced declines in the likelihood of obtaining DPT 3, measles as well as BCG, reductions ranged from 1.3 percentage points (Senegal) to 5.5 percentage points (Bangladesh).ConclusionSIA exposure reduced routine vaccination rates in study countries. Efforts should be made to limit the detrimental impact of SIAs on the services provided by routine health systems.