As of April 25, 2022, Mauritius was the African country with the highest number of coronavirus (COVID-19) doses secured per capita. The country had received **** COVID-19 vaccine doses per capita through bilateral agreements, donations, and the COVAX initiative. Seychelles and Rwanda followed with **** and **** doses per capita, respectively.

As of January 18, 2023, Portugal had the highest COVID-19 vaccination rate in Europe having administered 272.78 doses per 100 people in the country, while Malta had administered 258.49 doses per 100. The UK was the first country in Europe to approve the Pfizer/BioNTech vaccine for widespread use and began inoculations on December 8, 2020, and so far have administered 224.04 doses per 100. At the latest data, Belgium had carried out 253.89 doses of vaccines per 100 population. Russia became the first country in the world to authorize a vaccine - named Sputnik V - for use in the fight against COVID-19 in August 2020. As of August 4, 2022, Russia had administered 127.3 doses per 100 people in the country.

The seven-day rate of cases across Europe shows an ongoing perspective of which countries are worst affected by the virus relative to their population. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

Context

All about an attempt to end the pandemic across the globe with the help of vaccinations for COVID-19. It is important to track and understand the effort that is in progress across the globe to administer doses of vaccinations. There could be many sources of information. This is one of the sources from Bloomberg that is captured and presented here. Additionally, I have tried to include the GDP per capita per country from Wiki so that we can see how that is influencing the vaccination progress.

Content

There are two files. a) Latest Global Covid-19 Vaccine tracker of all the countries and regions in the World as of September 11, 2021 b) GDP information per capita per country

Attribute Information (COVID19 vaccination Tracker file)

• Countries and regions - Name of countries
• Doses administered - Number of vaccine doses administered
• Enough for % of people - Number of vaccine doses administered as a % of population
• Percentage of population with 1+ dose - Percentage of the population vaccinated with at least 1+ dose
• Percentage of the population fully vaccinated - Percentage of the population fully vaccinated
• Daily rate of doses administered - Daily rate of doses administered

Attribute Information (for GDP file per country per capita)

• Country
• Subregion (Western Europe, Northern Europe etc.)
• Region (Europe, Asia etc.)
• GDP estimate $ as per IMF
• Year for IMF
• GDP estimate $ as per UN
• Year for UN
• GDP estimate $ as per World Bank
• Year for World Bank

Source

URL1: https://www.bloomberg.com/graphics/covid-vaccine-tracker-global-distribution/ URL2: https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)_per_capita

Inspiration

The path to immunity and hope to get back to normalcy by tracking and analyzing the latest updates on vaccinations across the globe. As we gear up to end the pandemic, the vaccination tracker can help us answer the following questions.

• What are the Top N countries/regions where vaccinations are administered?
• What are the Top N countries/regions with fully vaccinated people?
• What are the Top N countries/regions with at least 1+ doses administered?
• What is the access to vaccines - by least wealthy and most wealthy countries? (based on GDP per capita per country data)
• What is the average daily rate of the dose administered? Which countries are Top N and Bottom N? Which countries are above and below the World average? and many more...

Thank you for reading.

Please give your feedback/upvote/comments if you find this useful and download.

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

BackgroundThe rapid development and rollout of COVID-19 vaccines helped reduce the pandemic’s mortality burden. The vaccine rollout, however, has been uneven; it is well known that vaccination rates tend to be lower in lower income countries. Vaccine uptake, however, ultimately depends on the willingness of individuals to get vaccinated. If vaccine confidence is low, then uptake will be low, regardless of country income level. We investigated the impact on country-level COVID-19 vaccination rates of both national income and vaccine hesitancy.MethodsWe estimated a linear regression model of COVID-19 vaccine uptake across 145 countries; this cross-sectional model was estimated at each of four time points: 6, 12, 18, and 24 months after the onset of global vaccine distribution. Vaccine uptake reflects the percentage of the population that had completed their primary vaccination series at the time point. Covariates include per capita GDP, an estimate of the percentage of country residents who strongly disagreed that vaccines are safe, and a variety of control variables. Next, we estimated these models of vaccine uptake by country income (countries below, and above the international median per capita GDP) to examine whether the impact of vaccine hesitancy varies by country income.ResultsWe find that GDP per capita has a pronounced impact on vaccine uptake at 6 months after global rollout. After controlling for other factors, there was a 22 percentage point difference in vaccination rates between the top 20% and the bottom 20% of countries ranked by per capita GDP; this difference grew to 38% by 12 months. The deleterious impact of distrust of vaccine safety on vaccine uptake became apparent by 12 months and then increased over time. At 24 months, there was a 17% difference in vaccination rates between the top 20% and the bottom 20% of countries ranked by distrust. The income stratified models reveal that the deleterious impact of vaccine distrust on vaccine uptake at 12 and 24 months is particularly evident in lower income countries.ConclusionsOur study highlights the important role of both national income and vaccine hesitancy in determining COVID-19 vaccine uptake globally. There is a need to increase the supply and distribution of pandemic vaccines to lower-income countries, and to take measures to improve vaccine confidence in these countries.

Estimated regression models of percentage of population fully vaccinated at 6, 12, 18 and 24 months post global roll-out.

As of March 20, 2023, around 391 doses of COVID-19 vaccines per 100 people in Cuba had been administered, one of the highest COVID-19 vaccine dose rates of any country worldwide. This statistic shows the rate of COVID-19 vaccine doses administered worldwide as of March 20, 2023, by country or territory.

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.

About this Dataset

The data contains the following information:

Country- this is the country for which the vaccination information is provided; Country ISO Code - ISO code for the country; Date - date for the data entry; for some of the dates we have only the daily vaccinations, for others, only the (cumulative) total; Total number of vaccinations - this is the absolute number of total immunizations in the country; Total number of people vaccinated - a person, depending on the immunization scheme, will receive one or more (typically 2) vaccines; at a certain moment, the number of vaccination might be larger than the number of people; Total number of people fully vaccinated - this is the number of people that received the entire set of immunization according to the immunization scheme (typically 2); at a certain moment in time, there might be a certain number of people that received one vaccine and another number (smaller) of people that received all vaccines in the scheme; Daily vaccinations (raw) - for a certain data entry, the number of vaccination for that date/country; Daily vaccinations - for a certain data entry, the number of vaccination for that date/country; Total vaccinations per hundred - ratio (in percent) between vaccination number and total population up to the date in the country; Total number of people vaccinated per hundred - ratio (in percent) between population immunized and total population up to the date in the country; Total number of people fully vaccinated per hundred - ratio (in percent) between population fully immunized and total population up to the date in the country; Number of vaccinations per day - number of daily vaccination for that day and country; Daily vaccinations per million - ratio (in ppm) between vaccination number and total population for the current date in the country; Vaccines used in the country - total number of vaccines used in the country (up to date); Source name - source of the information (national authority, international organization, local organization etc.); Source website - website of the source of information;

Tasks: Track the progress of COVID-19 vaccination What vaccines are used and in which countries? What country is vaccinated more people? What country is vaccinated a larger percent from its population?

This data is valuble in relation to the health, financial, and engineering sectors.

Category

Health & Medicine

Keywords

Health,Medicine,covid-19,dataset,progress

Row Count

5824

Price

$120.00

Summary statistics for outcome and explanatory variables.

Sources of the variables that appear in the regression models.

As of January 17, 2023, 96.3 percent of adults in Ireland had been fully vaccinated against COVID-19. According to the manufacturers of the majority of COVID-19 vaccines currently in use in Europe, being fully vaccinated is when a person receives two doses of the vaccine. In Portugal, 94.2 percent of adults had received a full course of the COVID-19 vaccination, as well as 93.9 percent of those in Malta had been fully vaccinated. On the other hand, only 35.8 percent of adults in Bulgaria had been fully vaccinated.

Furthermore, the seven-day rate of cases across Europe shows which countries are currently worst affected by the situation. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

IntroductionInequitable access to COVID-19 vaccines among countries is a pressing global health issue. Factors such as economic power, political power, political stability, and health system strength contribute to disparities in vaccine distribution. This study aims to assess the inequality in vaccine distribution among countries based on these factors and identify their relationship with COVID-19 vaccine distribution.MethodsA Concentration Index (CI) analysis was conducted to evaluate inequalities in the distribution of COVID-19 vaccines among countries based on four separate variables: GDP per capita, political stability (PS), World Power Index (WPI), and Universal Health Coverage (UHC). Additionally, Multiple Linear Regression (MLR) analysis was employed to explore the relationship between vaccine distribution and these independent variables. Two vaccine distribution variables were utilized for result reliability.ResultsThe analysis revealed significant inequalities in COVID-19 vaccine distribution according to the countries’ GDP/capita, PS, WPI, and UHC. However, the multiple linear regression analysis showed that there is no significant relationship between COVID-19 vaccine distribution and the countries’ GDP/capita and that UHC is the most influential factor impacting COVID-19 vaccine distribution and accessibility.DiscussionThe findings underscore the complex interplay between economic, political, and health system factors in shaping vaccine distribution patterns. To improve the accessibility to vaccines in future pandemics, Global Health Governance (GHG) and countries should consider working on three areas; enhance political stabilities in countries, separate the political power from decision-making at the global level and most importantly support countries to achieve UHC.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

COVID-19 Stats & Trends

Context

This dataset seeks to provide insights into what has changed due to policies aimed at combating COVID-19 and evaluate the changes in community activities and its relation to reduced confirmed cases of COVID-19. The reports chart movement trends, compared to an expected baseline, over time (from 2020/02/15 to 2020/02/05) by geography (across 133 countries), as well as some other stats about the country that might help explain the evolution of the disease.

Content

1. Grocery & Pharmacy: Mobility trends for places like grocery markets, food warehouses, farmers' markets, specialty food shops, drug stores, and pharmacies.
2. Parks: Mobility trends for places like national parks, public beaches, marinas, dog parks, plazas, and public gardens.
3. Residential: Mobility trends for places of residence.
4. Retail & Recreation: Mobility trends for places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters.
5. Transit stations: Mobility trends for places like public transport hubs such as subway, bus, and train stations.
6. Workplaces: Mobility trends for places of work.
7. Total Cases: Total number of people infected with the SARS-CoV-2.
8. Fatalities: Total number of deaths caused by CoV-19.
9. Government Response Stringency Index: Additive score of nine indicators of government response to CoV-19: School closures, workplace closures, cancellation of public events, public information campaigns, stay at home policies, restrictions on internal movement, international travel controls, testing policy, and contact tracing.
10. COVID-19 Testing: Total number of tests performed.
11. Total Vaccinations: Total number of shots given.
12. Total People Vaccinated: Total number of people given a shot.
13. Total People Fully Vaccinated: Total number of people fully vaccinated (might require two shots of some vaccines).
14. Population: Total number of inhabitants.
15. Population Density per km2: Number of human inhabitants per square kilometer.
16. Health System Index: Overall performance of the health system.
17. Human Development Index (HDI): Summary index based on life expectancy at birth, expected years of schooling for children and mean years of schooling for adults, and GNI per capita.
18. GDP (PPP) per capita: Gross Domestic Product (GDP) per capita based on Purchasing Power Parity (PPP), taking into account the relative cost of local goods, services and inflation rates of the country, rather than using international market exchange rates, which may distort the real differences in per capita income.
19. Elderly Population (percentage): Percentage of the population above the age of 65 years old.

References & Acknowledgements

Bing COVID-19 data. Available at: https://github.com/microsoft/Bing-COVID-19-Data COVID-19 Community Mobility Report. Available at: https://www.google.com/covid19/mobility/ COVID-19: Government Response Stringency Index. Available at: https://ourworldindata.org/grapher/covid-stringency-index Coronavirus (COVID-19) Testing. Available at: https://github.com/owid/covid-19-data/blob/master/public/data/testing/covid-testing-all-observations.csv Coronavirus (COVID-19) Vaccination. Available at: https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv List of countries and dependencies by population. Available at: https://www.kaggle.com/tanuprabhu/population-by-country-2020 List of countries and dependencies by population density. Available at: https://www.kaggle.com/tanuprabhu/population-by-country-2020 List of countries by Human Development Index. Available at: http://hdr.undp.org/en/data Measuring Overall Health System Performance. Available at: https://www.who.int/healthinfo/paper30.pdf?ua=1 List of countries by GDP (PPP) per capita. Available at: https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD List of countries by age structure (65+). Available at: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS

Authors

• Diogo Silva, up201706892@fe.up.pt

Context

Covid-19 Data collected from various sources on the internet. This dataset has daily level information on the number of affected cases, deaths, and recovery from the 2019 novel coronavirus. Please note that this is time-series data and so the number of cases on any given day is the cumulative number.

Content

The dataset includes 28 files scrapped from various data sources mainly the John Hopkins GitHub repository, the ministry of health affairs India, worldometer, and Our World in Data website. The details of the files are as follows

• countries-aggregated.csv A simple and cleaned data with 5 columns with self-explanatory names. -covid-19-daily-tests-vs-daily-new-confirmed-cases-per-million.csv A time-series data of daily test conducted v/s daily new confirmed case per million. Entity column represents Country name while code represents ISO code of the country. -covid-contact-tracing.csv Data depicting government policies adopted in case of contact tracing. 0 -> No tracing, 1-> limited tracing, 2-> Comprehensive tracing. -covid-stringency-index.csv The nine metrics used to calculate the Stringency Index are school closures; workplace closures; cancellation of public events; restrictions on public gatherings; closures of public transport; stay-at-home requirements; public information campaigns; restrictions on internal movements; and international travel controls. The index on any given day is calculated as the mean score of the nine metrics, each taking a value between 0 and 100. A higher score indicates a stricter response (i.e. 100 = strictest response). -covid-vaccination-doses-per-capita.csv A total number of vaccination doses administered per 100 people in the total population. 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). -covid-vaccine-willingness-and-people-vaccinated-by-country.csv Survey who have not received a COVID vaccine and who are willing vs. unwilling vs. uncertain if they would get a vaccine this week if it was available to them. -covid_india.csv India specific data containing the total number of active cases, recovered and deaths statewide. -cumulative-deaths-and-cases-covid-19.csv A cumulative data containing death and daily confirmed cases in the world. -current-covid-patients-hospital.csv Time series data containing a count of covid patients hospitalized in a country -daily-tests-per-thousand-people-smoothed-7-day.csv Daily test conducted per 1000 people in a running week average. -face-covering-policies-covid.csv Countries are grouped into five categories: 1->No policy 2->Recommended 3->Required in some specified shared/public spaces outside the home with other people present, or some situations when social distancing not possible 4->Required in all shared/public spaces outside the home with other people present or all situations when social distancing not possible 5->Required outside the home at all times regardless of location or presence of other people -full-list-cumulative-total-tests-per-thousand-map.csv Full list of total tests conducted per 1000 people. -income-support-covid.csv Income support captures if the government is covering the salaries or providing direct cash payments, universal basic income, or similar, of people who lose their jobs or cannot work. 0->No income support, 1->covers less than 50% of lost salary, 2-> covers more than 50% of the lost salary. -internal-movement-covid.csv Showing government policies in restricting internal movements. Ranges from 0 to 2 where 2 represents the strictest. -international-travel-covid.csv Showing government policies in restricting international movements. Ranges from 0 to 2 where 2 represents the strictest. -people-fully-vaccinated-covid.csv Contains the count of fully vaccinated people in different countries. -people-vaccinated-covid.csv Contains the total count of vaccinated people in different countries. -positive-rate-daily-smoothed.csv Contains the positivity rate of various countries in a week running average. -public-gathering-rules-covid.csv Restrictions are given based on the size of public gatherings as follows: 0->No restrictions 1 ->Restrictions on very large gatherings (the limit is above 1000 people) 2 -> gatherings between 100-1000 people 3 -> gatherings between 10-100 people 4 -> gatherings of less than 10 people -school-closures-covid.csv School closure during Covid. -share-people-fully-vaccinated-covid.csv Share of people that are fully vaccinated. -stay-at-home-covid.csv Countries are grouped into four categories: 0->No measures 1->Recommended not to leave the house 2->Required to not leave the house with exceptions for daily exercise, grocery shopping, and ‘essent...

As of April 26, 2023, around 81.3 percent of the U.S. population had received at least one dose of a COVID-19 vaccination. This statistic shows the percentage of the population in the United States who had been given a COVID-19 vaccination as of April 26, 2023, by state or territory.

By August 2024, Cuba had administered the largest number of vaccines against COVID-19 per 100 inhabitants in the Latin American region, followed by Chile and Peru. According to recent estimates, the Caribbean country applied around 410 doses per 100 population, accounting for one of the largest vaccination rates observed not only in the Latin American region, but worldwide. In comparison, Haiti registered the lowest vaccination rate within the region, with only 5.87 doses administered per 100 inhabitants. Booster shots started To reinforce the immune protection against the fast spread of the SARS-CoV-2, governments began to introduce booster shots in their immunization programs aiming at strengthening people’s immune response against new contagious COVID-19 variants. In Latin America, Cuba was leading on booster shots relative to its population among a selection of countries, with around 88 percent of the population receiving the extra dose. In comparison, these numbers are higher than those for the European Union and the United States. Pharmaceutical research continues As Omicron becomes more prominent worldwide, and recombinant variants emerge, research efforts to prevent and control the disease continue to progress. As of June 2022, there were around 2,700 clinical trials to treat COVID-19 and 1,752 COVID-19 vaccines trials in clinical development. Other studies were focused on mild, moderate and severe COVID-19, complication support, and post-COVID symptoms, among others.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Context

Data is collected daily from Our World in Data GitHub repository for covid-19, merged and uploaded.

Content

The data contains the following information: * **Country **- this is the country for which the vaccination information is provided;
* Country ISO Code - ISO code for the country;
* **Date **- date for the data entry; for some of the dates we have only the daily vaccinations, for others, only the (cumulative) total;
* Total number of vaccinations - this is the absolute number of total immunizations in the country;
* Total number of people vaccinated - a person, depending on the immunization scheme, will receive one or more (typically 2) vaccines; at a certain moment, the number of vaccination might be larger than the number of people;
* Total number of people fully vaccinated - this is the number of people that received the entire set of immunization according to the immunization scheme (typically 2); at a certain moment in time, there might be a certain number of people that received one vaccine and another number (smaller) of people that received all vaccines in the scheme;
* Daily vaccinations (raw) - for a certain data entry, the number of vaccination for that date/country;
* Daily vaccinations - for a certain data entry, the number of vaccination for that date/country;
* Total vaccinations per hundred - ratio (in percent) between vaccination number and total population up to the date in the country;
* Total number of people vaccinated per hundred - ratio (in percent) between population immunized and total population up to the date in the country;
* Total number of people fully vaccinated per hundred - ratio (in percent) between population fully immunized and total population up to the date in the country;
* Number of vaccinations per day - number of daily vaccination for that day and country;
* Daily vaccinations per million - ratio (in ppm) between vaccination number and total population for the current date in the country;
* Vaccines used in the country - total number of vaccines used in the country (up to date);
* Source name - source of the information (national authority, international organization, local organization etc.);
* Source website - website of the source of information;

Acknowledgements

I would like to specify that I am only making available Our World in Data collected data about vaccinations to Kagglers. My contribution is very small, just daily collection, merge and upload of the updated version, as maintained by Our World in Data in their GitHub repository.

Inspiration

Track COVID-19 vaccination in the World, answer instantly to your questions:
- Which country is using what vaccine?
- In which country the vaccination programme is more advanced?
- Where are vaccinated more people per day? But in terms of percent from entire population ?

The global vaccine market, including COVID-19 vaccines, is a dynamic and rapidly evolving sector. With a 2025 market size of $144.18 billion (USD) and a projected Compound Annual Growth Rate (CAGR) of 3.5% from 2025 to 2033, substantial growth is anticipated. This expansion is driven by several factors, including increasing government investments in vaccine research and development, rising incidence of vaccine-preventable diseases, growing awareness of the importance of vaccination, and the lasting impact of the COVID-19 pandemic, which spurred significant advancements in vaccine technology and manufacturing capabilities. The market is segmented by vaccine type (e.g., live attenuated, inactivated, conjugate, recombinant, mRNA), disease indication (e.g., influenza, measles, polio, COVID-19), and route of administration. While the initial surge in COVID-19 vaccine demand has somewhat subsided, ongoing booster campaigns and the development of next-generation vaccines targeting emerging variants continue to fuel market growth. Furthermore, the increasing prevalence of chronic diseases like cancer and age-related illnesses is driving demand for preventative vaccines. Major players like Pfizer, Sanofi, GSK, and Johnson & Johnson are shaping the market landscape, continually innovating to improve vaccine efficacy, safety, and delivery mechanisms. However, the market also faces certain restraints, such as stringent regulatory requirements, potential adverse events, vaccine hesitancy in some populations, and the need for robust cold chain infrastructure for effective distribution, particularly in developing countries. The ongoing evolution of viral pathogens and the need for continuous development of new and improved vaccines presents both challenges and opportunities for market expansion. The geographical distribution of market share is likely skewed towards developed nations with higher per capita incomes and access to advanced healthcare infrastructure, although emerging economies are expected to show significant growth in the coming years, driven by increased vaccination programs and rising disposable incomes. This necessitates a strategic focus on improving vaccine accessibility and affordability globally to ensure broad-based protection.