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...

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

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 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.

About Dataset: WHO COVID-19 Global Data

This dataset provides comprehensive information on the global COVID-19 pandemic as reported to the World Health Organization (WHO). The dataset is available in comma-separated values (CSV) format and includes the following fields:

Daily cases and deaths by date reported to WHO: WHO-COVID-19-global-data.csv

• Date_reported (Date): The date of reporting to WHO.
• Country_code (String): The ISO Alpha-2 country code.
• Country (String): The name of the country, territory, or area.
• WHO_region (String): The WHO regional office to which the country belongs. WHO Member States are grouped into six WHO regions, including AFRO (Regional Office for Africa), AMRO (Regional Office for the Americas), SEARO (Regional Office for South-East Asia), EURO (Regional Office for Europe), EMRO (Regional Office for the Eastern Mediterranean), and WPRO (Regional Office for the Western Pacific).
• New_cases (Integer): The number of new confirmed cases reported on a given day. This is calculated by subtracting the previous cumulative case count from the current cumulative case count.
• Cumulative_cases (Integer): The total cumulative confirmed cases reported to WHO up to the specified date.
• New_deaths (Integer): The number of new confirmed deaths reported on a given day. Similar to new cases, this is calculated by subtracting the previous cumulative death count from the current cumulative death count.- Cumulative_deaths (Integer): The total cumulative confirmed deaths reported to WHO up to the specified date.

In addition to the COVID-19 case and death data, this dataset also includes valuable information related to COVID-19 vaccinations. The vaccination data consists of the following fields:

Vaccination Data Fields: vaccination-data.csv

• COUNTRY (String): Country, territory, or area.
• ISO3 (String): ISO Alpha-3 country code.
• WHO_REGION (String): The WHO regional office to which the country belongs.
• DATA_SOURCE (String): Indicates the data source, which can be either "REPORTING" (Data reported by Member States or sourced from official reports) or "OWID" (Data sourced from Our World in Data COVID-19 Vaccinations).
• DATE_UPDATED (Date): Date of the last update.
• TOTAL_VACCINATIONS (Integer): Cumulative total vaccine doses administered.
• PERSONS_VACCINATED_1PLUS_DOSE (Decimal): Cumulative number of persons vaccinated with at least one dose.
• TOTAL_VACCINATIONS_PER100 (Integer): Cumulative total vaccine doses administered per 100 population.
• PERSONS_VACCINATED_1PLUS_DOSE_PER100 (Decimal): Cumulative persons vaccinated with at least one dose per 100 population.
• PERSONS_LAST_DOSE (Integer): Cumulative number of persons vaccinated with a complete primary series.
• PERSONS_LAST_DOSE_PER100 (Decimal): Cumulative number of persons vaccinated with a complete primary series per 100 population.
• VACCINES_USED (String): Combined short name of the vaccine in the format "Company - Product name."
• FIRST_VACCINE_DATE (Date): Date of the first vaccinations, equivalent to the start/launch date of the first vaccine administered in a country.
• NUMBER_VACCINES_TYPES_USED (Integer): Number of vaccine types used per country, territory, or area.
• PERSONS_BOOSTER_ADD_DOSE (Integer): Cumulative number of persons vaccinated with at least one booster or additional dose.
• PERSONS_BOOSTER_ADD_DOSE_PER100 (Decimal): Cumulative number of persons vaccinated with at least one booster or additional dose per 100 population.

In addition to the vaccination data, a separate dataset containing vaccination metadata is available, including information about vaccine names, product names, company names, authorization dates, start and end dates of vaccine rollout, and more.

Vaccination metadata Fields: vaccination-metadata.csv

• ISO3 (String): ISO Alpha-3 country code
• VACCINE_NAME (String): Combined short name of vaccine: "Company - Product name" (see below)
• PRODUCT_NAME (String): Name or label of vaccine product, or type of vaccine (if unnamed).
• COMPANY_NAME (String): Marketing authorization holder of vaccine product.
• FIRST_VACCINE_DATE (Date): Date of first vaccinations. Equivalent to start/launch date of the first vaccine administered in a country.
• AUTHORIZATION_DATE (Date): Date vaccine product was authorized for use in the country, territory, area.
• START_DATE (Date): Start/launch date of vaccination with vaccine type (excludes vaccinations during clinical trials).
• END_DATE (Date): End date of vaccine rollout
• COMMENT (String): Comments related to vaccine rollout
• DATA_SOURCE (String): Indicates data source - REPORTING: Data reported by Member States, or sourced from official re...

The number of COVID-19 vaccination doses administered per 100 people in the World rose to 168 as of Oct 27 2023. This dataset includes a chart with historical data for World Coronavirus Vaccination Rate.

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...

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

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...

COVID-19 Vaccination data as reported by the County's health department. On May 19, 2023, with the rescinding of the COVID-19 public health emergency, changes in data and reporting mechanisms prompted a change in the municipal counts. Data attributes listed as 'Archive Only' within the below description are reflected only in data prior to May 19, 2023. These files are maintained as 'Archive' files within this repository.

This dataset contains 3 tables:

1. Allegheny County COVID-19 Vaccine Individual Data: Contains vaccination information on an individual level for all three vaccination statuses
2. Allegheny County COVID-19 Vaccine Municipal Data: Contains counts of bivalent booster vaccinations for all neighborhoods in Allegheny County
3. Allegheny County COVID-19 Vaccine Historical Municipal Data: Contains historical counts for each vaccination status for all neighborhoods in Allegheny County up through May 2023, then limited to counts of bivalent booster vaccinations following this cut-off.

Types of Vaccination Status:

1. Partially Vaccinated [Individual and Archive Only]: If an individual has not completed their primary series with either 1 of 2-dose series or 2 of 3-dose series for children under the age of 5 years
2. Completed Primary Series (formerly Fully Vaccinated) [Individual and Archive Only]: If an individual has completed their primary series with the 1-dose J&J series, 2-dose series, or 3-dose series for children under 5 years.
3. Bivalent Booster: If an individual has obtained their bivalent booster dose.

Due to minor discrepancies in the Municipal boundary and the City of Pittsburgh Neighborhood files individuals whose City Neighborhood cannot be identified are be counted as “Undefined (Pittsburgh)”.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

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:

How many people have received a coronavirus vaccine?

Tracking COVID-19 vaccination rates is crucial to understand the scale of protection against the virus, and how this is distributed across the global population.

A global, aggregated database on COVID-19 vaccination rates is essential to monitor progress, but it is unfortunately not yet available. This dataset provides the last weekly update of vaccination rates.

Last update

June 2021

Content

Colums description: 1. iso_code: ISO 3166-1 alpha-3 – three-letter country codes 2. continent: Continent of the geographical location 3. location: Geographical location 4. date: Date of observation 5. total_cases: Total confirmed cases of COVID-19 6. new_cases: New confirmed cases of COVID-19 7. new_cases_smoothed: New confirmed cases of COVID-19 (7-day smoothed) 8. total_deaths: Total deaths attributed to COVID-19 9. new_deaths: New deaths attributed to COVID-19 10. new_deaths_smoothed: New deaths attributed to COVID-19 (7-day smoothed) 11. total_cases_per_million: Total confirmed cases of COVID-19 per 1,000,000 people 12. new_cases_per_million: New confirmed cases of COVID-19 per 1,000,000 people 13. new_cases_smoothed_per_million: New confirmed cases of COVID-19 (7-day smoothed) per 1,000,000 people 14. total_deaths_per_million: Total deaths attributed to COVID-19 per 1,000,000 people 15. new_deaths_per_million: New deaths attributed to COVID-19 per 1,000,000 people 16. new_deaths_smoothed_per_million: New deaths attributed to COVID-19 (7-day smoothed) per 1,000,000 people 17. reproduction_rate: Real-time estimate of the effective reproduction rate (R) of COVID-19. See http://trackingr-env.eba-9muars8y.us-east-2.elasticbeanstalk.com/FAQ 18. icu_patients: Number of COVID-19 patients in intensive care units (ICUs) on a given day 19. icu_patients_per_million: Number of COVID-19 patients in intensive care units (ICUs) on a given day per 1,000,000 people 20. hosp_patients: Number of COVID-19 patients in hospital on a given day 21. hosp_patients_per_million: Number of COVID-19 patients in hospital on a given day per 1,000,000 people 22. weekly_icu_admissions: Number of COVID-19 patients newly admitted to intensive care units (ICUs) in a given week 23. weekly_icu_admissions_per_million: Number of COVID-19 patients newly admitted to intensive care units (ICUs) in a given week per 1,000,000 people 24. weekly_hosp_admissions: Number of COVID-19 patients newly admitted to hospitals in a given week 25. weekly_hosp_admissions_per_million: Number of COVID-19 patients newly admitted to hospitals in a given week per 1,000,000 people 26. total_tests: Total tests for COVID-19 27. new_tests: New tests for COVID-19 28. new_tests_smoothed: New tests for COVID-19 (7-day smoothed). For countries that don't report testing data on a daily basis, we assume that testing changed equally on a daily basis over any periods in which no data was reported. This produces a complete series of daily figures, which is then averaged over a rolling 7-day window 29. total_tests_per_thousand: Total tests for COVID-19 per 1,000 people 30. new_tests_per_thousand: New tests for COVID-19 per 1,000 people 31. new_tests_smoothed_per_thousand: New tests for COVID-19 (7-day smoothed) per 1,000 people 32. tests_per_case: Tests conducted per new confirmed case of COVID-19, given as a rolling 7-day average (this is the inverse of positive_rate) 33. positive_rate: The share of COVID-19 tests that are positive, given as a rolling 7-day average (this is the inverse of tests_per_case) 34. tests_units: Units used by the location to report its testing data 35. total_vaccinations: Number of COVID-19 vaccination doses administered 36. total_vaccinations_per_hundred: Number of COVID-19 vaccination doses administered per 100 people 37. stringency_index: Government Response Stringency Index: composite measure based on 9 response indicators including school closures, workplace closures, and travel bans, rescaled to a value from 0 to 100 (100 = strictest response) 38. population: Population in 2020 39. population_density: Number of people divided by land area, measured in square kilometers, most recent year available 40. median_age: Median age of the population, UN projection for 2020 41. aged_65_older: Share of the population that is 65 years and older, most recent year available 42. aged_70_older: Share of the population that is 70 years and older in 2015 43. gdp_per_capita: Gross domestic product at purchasing power parity (constant 2011 international dollars), most recent year available 44. extreme_poverty: Share of the population living in extreme poverty, most recent year available since 2010 45. cardiovasc_death_rate: Death rate from cardiovascular disease in 2017 (annual number of deaths per 100,000 people) 46. diabetes_prevalence: Diabetes prevalence (% of population aged 20 to 79) in 2017 47. female...

Regarding all Vaccination Data The date of Last Update is 4/21/2023. Additionally on 4/27/2023 several COVID-19 datasets were retired and no longer included in public COVID-19 data dissemination. See this link for more information https://imap.maryland.gov/pages/covid-data Summary The cumulative number of COVID-19 vaccinations percent age group population: 16-17; 18-49; 50-64; 65 Plus. Description COVID-19 - Vaccination Percent Age Group Population data layer is a collection of COVID-19 vaccinations that have been reported each day into ImmuNet. COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county. Terms of Use The Spatial Data, and the information therein, (collectively the Data) is provided as is without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata. This map is for planning purposes only. MEMA does not guarantee the accuracy of any forecast or predictive elements.

IntroductionVaccination rates for the COVID-19 vaccine have recently been stagnant worldwide. We aim to analyze the potential patterns of vaccination development from the first three doses to reveal the possible trends of the next round of vaccination and further explore the factors influencing vaccination in the selected populations.MethodsOn July 2022, a stratified multistage random sampling method in the survey was conducted to select 6,781 people from 4 provinces China, who were above the age of 18 years. Participants were divided into two groups based on whether they had a chronic disease. The data were run through Cochran-Armitage trend test and multivariable regression analyses.ResultsA total of 957 participants with chronic disease and 5,454 participants without chronic disease were included in this survey. Vaccination rates for the first, second and booster doses in chronic disease population were93.70% (95% CI: 92.19–95.27%), 91.12% (95%CI: 94.43–95.59%), and 83.18% (95%CI: 80.80–85.55%) respectively. By contrast, the first, second and booster vaccination rates for the general population were 98.02% (95% CI: 97.65–98.39%), 95.01% (95% CI: 94.43–95.59%) and 85.06% (95% CI: 84.11–86.00%) respectively. The widening gap in vaccination rates was observed as the number of vaccinations increases. Higher self-efficacy was a significant factor in promoting vaccination, which has been observed in all doses of vaccines. Higher education level, middle level physical activity and higher public prevention measures play a positive role in vaccination among the general population, while alcohol consumption acts as a significant positive factor in the chronic disease population (p < 0.05).ConclusionAs the number of vaccinations increases, the trend of decreasing vaccination rate is becoming more pronounced. In future regular vaccinations, we may face low vaccination rates as the increasing number of infections and the fatigue associated with the prolonged outbreak hamper vaccination. Measures need to be found to counter this downward trend such as improving the self-efficacy of the population.

The donation and sale of vaccines are diplomatic tools that have impact well beyond health policies. May Chinese Covid-related vaccine diplomacy be understood beyond reactive terms vis-à-vis power disputes with the West, in particularly the United States? We then scrutinize the drivers of China’s vaccine diplomacy, assessing whether Beijing privileged the expansion of its diplomatic leverage in the Global South. By employing logit and tobit models in the analysis of a cross-sectional dataset covering 213 countries, we examine the probability of countries receiving vaccines from China. We find that low-income states, in particular, and middle-income ones and those with more Covid deaths were more likely to receive vaccines through either donations or purchases. For donations, states that integrate the Belt and Road Initiative (BRI) and/or oppose the United States at the United Nations General Assembly (UNGA) were also privileged. China’s vaccine diplomacy has therefore a twofold purpose. First, the expansion of the country’s soft power in the Global South. Second, the consolidation of the BRI bilateral ties and an anti-US allied network. Hence, current global health initiatives cannot be detached from debates on the contestation of the liberal international order (LIO) and China’s dual role as a responsible stakeholder and most successful emerging power that has the potential to challenge American hegemony. Moreover, the findings also suggest that bilateral donor-recipient flows may be less politicized than what prior works on development aid and health diplomacy have claimed.

A phase 1/2, open-label clinical trial in individuals, 18 years of age and older, who are in good health, have no known history of Coronavirus Disease 2019 (COVID-19) or Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, and meet all other eligibility criteria. This clinical trial is designed to assess the safety, reactogenicity and immunogenicity of a delayed (>/=12 weeks) vaccine boost on a range of Emergency Use Authorization (EUA)-dosed COVID-19 vaccines (mRNA-1273, and mRNA-1273.211 manufactured by ModernaTX, Inc.; BNT162b2 manufactured by Pfizer/BioNTech; or Ad26.COV2.S manufactured by Janssen Pharmaceuticals/Johnson & Johnson). This is an adaptive design and may add arms (and increase sample size) as vaccines are awarded EUA and/or variant lineage spike vaccines are manufactured or become available. Enrollment will occur at up to twelve domestic clinical research sites.

This study includes two cohorts. Cohort 1 will include approximately 880 individuals (50 subjects/group; Groups 1E-11E) greater than 18 years of age and older, stratified into two age strata (18-55 years and >/=56 years) who previously received COVID-19 vaccine at Emergency Use Authorization dosing (EUA) (two vaccinations of mRNA-1273 at the 100 mcg dose, two vaccinations of BNT162b2 at the 30 mcg dose, or one vaccination of Ad26.COV2.S at the 5x10^10 vp dose). Groups 15E-17E will enroll 60 subjects, split (approximately evenly) between age strata as able. Those subjects will be offered enrollment into this study >/=12 weeks after they received the last dose of their EUA vaccine. Subjects will receive a single open-label intramuscular (IM) injection of the designated delayed booster vaccine and will be followed through 12 months after vaccination: 1) Group 1E - previously EUA-dosed vaccination with Janssen - Ad26.COV.2.S at 5x10^10 vp followed by a 100-mcg dose of mRNA-1273, Group 4E - previously EUA-dosed vaccination with Janssen - Ad26.COV.2.S at 5x10^10 vp followed by a 5x10^10 vp dose of Ad26.COV2.S, Group 7E - previously EUA-dosed vaccination with Janssen - Ad26.COV.2.S 5x10^10 vp followed by a 30-mcg dose of BNT162b2, Group 10E - previously EUA-dosed vaccination with Janssen - Ad26.COV2-S 5x10^10 vp followed by a 100-mcg dose of mRNA-1273.211; Group 12E - previously EUA-dosed vaccination with Janssen - Ad26.COV2-S 5x10^10 vp followed by a 50-mcg dose of mRNA-1273; Group 15E - previously EUA-dosed vaccination with Janssen (two doses for Group 15E) - Ad26.COV2.S at 5x1010 vp followed by a dose of NVX-CoV2373 (5 mcg Prototype SARS-CoV-2 rS vaccine with 50 mcg Matrix-M); 2) Group 2E - previously EUA-dosed vaccination with Moderna - mRNA-1273 at 100 mcg for two doses followed by a 100-mcg dose of mRNA-1273, Group 5E - previously EUA-dosed vaccination with Moderna - mRNA-1273 at 100 mcg for two doses followed by a 5x10^10 vp dose of Ad26.COV2.S, Group 8E - previously EUA-dosed vaccination with Moderna - mRNA-1273 at 100 mcg for two doses followed by a 30-mcg dose of BNT162b2, Group 13E - previously EUA-dosed vaccination with Moderna - mRNA-1273 at 100 mcg for two doses followed by a 50-mcg dose of mRNA-1273; Group 16E - previously EUA-dosed vaccination with Moderna - mRNA-1273 at 100 mcg for two doses followed by a dose of NVX-CoV2373 (5 mcg Prototype SARS-CoV2 rS vaccine with 50 mcg Matrix-M); 3) Group 3E - previously EUA-dosed vaccination with Pfizer/BioNTech - BNT162b2 at 30 mcg for two doses followed by a 100-mcg dose of mRNA-1273. Group 6E - previously EUA-dosed vaccination with Pfizer/BioNTech - BNT162b2 at 30 mcg for two doses followed by a 5x10^10 vp dose of Ad26.COV2.S, Group 9E - previously EUA-dosed vaccination with Pfizer/BioNTech - BNT162b2 at 30 mcg for two doses followed by a 30-mcg dose of BNT162b2, Group 11E - previously EUA-dosed vaccination with Pfizer/BioNTech - BNT162b2 at 30 mcg for two doses followed by a 100-mcg dose of mRNA-1273.211. Group 14E - previously EUA-dosed vaccination with Pfizer/BioNTech - BNT162b2 at 30 mcg for two doses followed by a 50-mcg dose of mRNA-1273, Group 17E - previously EUA-dosed vaccination with Pfizer/BioNTech - BNT162b2 at 30 mcg for two doses followed by a dose of NVX-CoV2373 (5 mcg Prototype SARS-CoV2 rS vaccine with 50 mcg Matrix-M).

A telephone visit will occur one week after each primary EUA vaccination and one week after the booster dose. In person follow-up visits will occur on 14 days following completion of EUA vaccinations and on days 14, and 28 days after the booster dose, as well as 3, 6, and 12 months post the booster vaccination. Additional pools of subjects can be included if needed as additional COVID-19 vaccines are awarded EUA.

The primary objectives of this study are 1) to evaluate the safety and reactogenicity of delayed heterologous or homologous vaccine doses after EUA dosed vaccines, and 2) to evaluate the breadth of the humoral immune responses of heterologous and homologous delayed boost regimens following EUA dosing.

Disclaimer: As of January 2025, SPC will no longer provide updated information on COVID-19 cases and deaths. The information presented on this page is for reference only. For current epidemic and emerging disease alerts in the Pacific region, please visit: https://www.spc.int/epidemics/

Statistics from SPC's Public Health Division (PHD) on COVID-19 vaccination in Pacific Island Countries and Territories. Monitoring the impact of COVID-19 and the effectiveness of prevention and control strategies remains a public health priority. With the COVID-19 Public Health Emergency of International Concern declaration ending, some metrics have changed in frequency, source, or availability (i.e vaccination data). SPC will no longer continue to provide updated information on vaccination. The last update for this dataset was the 09 May 2023.

Find more Pacific data on PDH.stat.

IntroductionIt is clear that medical science has advanced much in the past few decades with the development of vaccines and this is even true for the novel coronavirus outbreak. By late 2020, COVID-19 vaccines were starting to be approved by national and global regulators, and across 2021, there was a global rollout of several vaccines. Despite rolling out vaccination programs successfully, there has been a cause of concern regarding uptake of vaccine due to vaccine hesitancy. In tackling the vaccine hesitancy and improving the overall vaccination rates, digital health literacy (DHL) could play a major role. Therefore, the aim of this study is to assess the digital health literacy and its relevance to the COVID-19 vaccination.MethodsAn internet-based cross-sectional survey was conducted from April to August 2021 using convenience sampling among people from different countries. Participants were asked about their level of intention to the COVID-19 vaccine. Participants completed the Digital Health Literacy Instrument (DHLI), which was adapted in the context of the COVID Health Literacy Network. Cross-tabulation and logistic regression were used for analysis purpose.ResultsOverall, the mean DHL score was 35.1 (SD = 6.9, Range = 12–48). The mean DHL score for those who answered “Yes” for “support for national vaccination schedule” was 36.1 (SD 6.7) compared to 32.5 (SD 6.8) for those who either answered “No” or “Don't know”. Factors including country, place of residence, education, employment, and income were associated with the intention for vaccination. Odds of vaccine intention were higher in urban respondents (OR-1.46; C.I.-1.30–1.64) than in rural respondents. Further, higher competency in assessing the relevance of online information resulted in significantly higher intention for vaccine uptake.ConclusionPriority should be given to improving DHL and vaccination awareness programs targeting rural areas, lower education level, lower income, and unemployed groups.

IntroductionHealthcare workers risk of exposure to the influenza virus in their work, is a high-risk group for flu infections. Thus WHO recommends prioritizing flu vaccination for them–an approach adopted by >40 countries and/or regions worldwide.MethodsCross-sectional studies on influenza vaccination rates among healthcare workers were collected from PubMed, EMBASE, CNKI, and CBM databases from inception to February 26, 2023. Influenza vaccination rates and relevant data for multiple logistic regression analysis, such as odds ratios (OR) and 95% confidence intervals (CI), were extracted.ResultsA total of 92 studies comprising 125 vaccination data points from 26 countries were included in the analysis. The meta-analysis revealed that the overall vaccination rate among healthcare workers was 41.7%. Further analysis indicated that the vaccination rate was 46.9% or 35.6% in low income or high income countries. Vaccination rates in the Americas, the Middle East, Oceania, Europe, Asia, and Africa were 67.1, 51.3, 48.7, 42.5, 28.5, and 6.5%, respectively. Influencing factors were age, length of service, education, department, occupation, awareness of the risk of influenza, and/or vaccines.ConclusionThe global influenza vaccination rate among healthcare workers is low, and comprehensive measures are needed to promote influenza vaccination among this population.Systematic review registrationwww.inplysy.com, identifier: 202350051.