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...

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

CDC COVID-19 Vaccine Tracker

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

By Nicky Forster [source]

About this dataset

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

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

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

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

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

How to use the dataset

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

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

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

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

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

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

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

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

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

Research Ideas

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

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...

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.

Context

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

Content

Data Collection

This dataset is a combination of the following three datasets:

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

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

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

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

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

Countries in this dataset:

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.

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.

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

ObjectivesWe assessed the knowledge and awareness of cervical cancer, HPV and HPV vaccine, and willingness and acceptability to vaccinate in sub-Saharan African (SSA) countries. We further identified countries that fulfill the two GAVI Alliance eligibility criteria to support nationwide HPV vaccination.MethodsWe conducted a systematic review of peer-reviewed studies on the knowledge and awareness of cervical cancer, HPV and HPV vaccine, and willingness and acceptability to vaccinate. Trends in Diphtheria-tetanus-pertussis (DTP3) vaccine coverage in SSA countries from 1990–2011 were extracted from the World Health Organization database.FindingsThe review revealed high levels of willingness and acceptability of HPV vaccine but low levels of knowledge and awareness of cervical cancer, HPV or HPV vaccine. We identified only six countries to have met the two GAVI Alliance requirements for supporting introduction of HPV vaccine: 1) the ability to deliver multi-dose vaccines for no less than 50% of the target vaccination cohort in an average size district, and 2) achieving over 70% coverage of DTP3 vaccine nationally. From 2008 through 2011 all SSA countries, with the exception of Mauritania and Nigeria, have reached or maintained DTP3 coverage at 70% or above.ConclusionThere is an urgent need for more education to inform the public about HPV, HPV vaccine, and cervical cancer, particularly to key demographics, (adolescents, parents and healthcare professionals), to leverage high levels of willingness and acceptability of HPV vaccine towards successful implementation of HPV vaccination programs. There is unpreparedness in most SSA countries to roll out national HPV vaccination as per the GAVI Alliance eligibility criteria for supporting introduction of the vaccine. In countries that have met 70% DTP3 coverage, pilot programs need to be rolled out to identify the best practice and strategies for delivering HPV vaccines to adolescents and also to qualify for GAVI Alliance support.

Original data collection of PHSM began on March 28, 2020. Since then, governments have implemented a wide variety of PHSM often with increasing nuance (e.g. with regards to the geographic or demographic targets of a given policy). While Version 1.0 of the dataset only released data from questions in the original survey, Version 1.1 releases data from new questions that have been added over the course of first year of data collection. For more information about the additional fields and options added to the dataset, please see our codebook update_level_var: More detailed information as to what dimension of a policy is being updated (i.e., strengthened or relaxed) pdf_link: Link to PDF of the original source used to document a policy institution_cat: Information as to whether a business or government service is considered essential or non-essential according to the government entity in charge of implementing a given PHSM institution_conditions: Information about what conditions a school, business or government service is allowed to open under (e.g., limited number of people allowed on premises) type_new_admin_coop: Information about the nature of a given cooperative effort if different governments decide to cooperate with each other (e.g. country A cooperates with country B) COVID-19 Vaccines: We have added new questions to capture various dimensions of the global COVID-19 Vaccine rollout including information on: The manufacturing firm (type_vac_cat) Whether vaccines are allowed to be mixed and matched (type_vac_mix) The regulatory status of a given COVID-19 vaccine (type vac reg) Information on the type of purchase order for COVID-19vaccines (type_vac_purchase) Information on the overall criteria used for deciding how to administer COVID-19 vaccines (type vac group). Information on the number of priority groups for COVID-19 Vaccine distribution, given that this is the criteria used for deciding how to administer COVID-19 vaccines (type_vac_group_rank) Information as to where COVID-19 vaccines are being administered (type_vac_loc) Information as to who is responsible for the economic cost of a given COVID-19 vaccine shot (type_vac_who_pays) Information as to what entity has been placed in primary charge for the COVID- 19 vaccination process (type_vac_dist_admin) Information as to the monetary resource devoted for a given COVID-19 vaccine policy ( typ_vac_cost_num, type_vac_cost_unit, type_vac_cost_scale, type_vac_gov_perc) Information as the volume of COVID-19 vaccines (e.g. shots) in question for a given COVID-19 vaccine policy (typeva amt_num, type_vac_amt_unit, type_vac_amt_scale, type_vac_amt_gov_perc) target_init_same: Whether the geographic target of a policy is the same as the policy initiator (e.g. target init same ==0 if lockdown policy implemented by government A in country A while target init same ==1 if an external border restrictions is im- plemented by government A against country B) target_intl_org: Which international organization a policy is targeted to, if applicable. 11 target_who_gen: Information as to what special populations (e.g. asylum seekers, in- digenous peoples) a policy targets, if applicable date_end_spec: Qualitative information on a policy's end date

Morbidity and mortality attributable to COVID-19 is devastating global health systems and economies. Bacillus Calmette Guérin (BCG) vaccination has been in use for many decades to prevent severe forms of tuberculosis in children. Studies have also shown a combination of improved long-term innate or trained immunity (through epigenetic reprogramming of myeloid cells) and adaptive responses after BCG vaccination, which leads to non-specific protective effects in adults. Observational studies have shown that countries with routine BCG vaccination programs have significantly less reported cases and deaths of COVID-19, but such studies are prone to significant bias and need confirmation. To date, in the absence of direct evidence, WHO does not recommend BCG for the prevention of COVID-19. This project aims to investigate in a timely manner whether and why BCG-revaccination can reduce infection rate and/or disease severity in health care workers during the SARS-CoV-2 outbreak in South Africa. These objectives will be achieved with a blinded, randomised controlled trial of BCG revaccination versus placebo in exposed front-line staff in hospitals in Cape Town. Observations will include the rate of infection with COVID-19 as well as the occurrence of mild, moderate or severe ambulatory respiratory tract infections, hospitalisation, need for oxygen, mechanical ventilation or death. HIV-positive individuals will be excluded. Safety of the vaccines will be monitored. A secondary endpoint is the occurrence of latent or active tuberculosis. Initial sample size and follow-up duration is at least 500 workers and 52 weeks. Statistical analysis will be model-based and ongoing in real time with frequent interim analyses and optional increases of both sample size or observation time, based on the unforeseeable trajectory of the South African COVID-19 epidemic, available funds and recommendations of an independent data and safety monitoring board. The study will be supported by a novel 3D lung organoid model of SARS-CoV-2 infection system that can mimic the cascade of immunological events after SARS-CoV-2 infection to determine and analyse the contribution of cellular components to the impact of BCG revaccination in this study. Given the immediate threat of the SARS-CoV-2 epidemic the trial has been designed as a pragmatic study with highly feasible endpoints that can be continuously measured. This allows for the most rapid identification of a beneficial outcome that would lead to immediate dissemination of the results, vaccination of the control group and outreach to the health authorities to consider BCG vaccination for all qualifying health care workers. Methods This dataset was collected in a clinical randomised control trial under the TASK008-BCG CORONA protocol. The trial was conducted in South Africa. This trial was registered with ClinicalTrials.gov, NCT04379336.

Vaccination against COVID-19 is making progress globally, but vaccine doses remain a rare commodity in many parts of the world. New virus variants mean that updated vaccines become available more slowly. Policymakers have defined criteria to regulate who gets priority access to the vaccination, such as age, health complications, or those who hold system-relevant jobs. But how does the public think about vaccine allocation? To explore those preferences, we surveyed respondents in Brazil, Germany, Italy, Poland, and the United States from September to December of 2020 using ranking and forced-choice tasks. We find that public preferences are consistent with expert guidelines prioritizing health care workers and people with medical preconditions. However, the public also considers those signing up early for vaccination and citizens of the country to be more deserving than later-comers and non-citizens. These results hold across measures, countries, and socio-demographic subgroups.

Global Export of Vaccines for Human Use by Country, 2023 Discover more data with ReportLinker!

Context

Data collected from Our World in Data GitHub repository for covid-19, merged and uploaded. Country level vaccination data is gathered and assembled in one single file. Then, this data file is merged with locations data file to include vaccination sources information. A second file, with manufacturers information, is included.

Content

The data (country vaccinations) 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; There is a second file added recently (country vaccinations by manufacturer), with the following columns:

• Location - country;

• Date - date;

• Vaccine - vaccine type;

• Total number of vaccinations - total number of vaccinations / current time and vaccine type. 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 ? Combine this dataset with COVID-19 World Testing Progress and COVID-19 Variants Worldwide Evolution to get more insights on the dynamics of the pandemics, as reflected in the interdependence of amount of testing performed, results of sequencing and vaccination campaigns.

This feature layer is part of SDGs Today. Please see sdgstoday.orgThe Launch and Scale Speedometer, led by the Duke Global Health Innovation Center, has tracked COVID-19 vaccine purchase agreements between November 2020 and June 2022. This dataset provides the most recent data on vaccine purchases and negotiations by individual countries and unilateral partnerships from 16 companies. Unilateral partnerships include the African Union, European Union, Latin America excluding Brazil, and COVAX, the global initiative aimed to produce, procure, and distribute vaccines to member countries.So far, 14.9 billion doses have been reserved. Confirmed doses are deals that have been signed and finalized. Potential doses include both deals that are under negotiation (not yet final) and also options for additional doses as part of existing confirmed deals.For more information, contact info@launchandscalefaster.org

Series Name: (S.4.C.13) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care information and education: Component 13: HPV Vaccine (percent)Series Code: SH_LGR_ACSRHEC13Release Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 5.6.2: Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and educationTarget 5.6: Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferencesGoal 5: Achieve gender equality and empower all women and girlsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

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.

Einstellungen zur Impfung gegen Covid-19. Themen: präferierter Impfzeitpunkt; Wichtigkeit der folgenden Gründe im Hinblick auf die Entscheidung, sich impfen zu lassen: Impfstoff wird bei der Beendigung der Pandemie helfen, Impfstoff wird den/die Befragte/n vor Covid-19 schützen, Impfstoff wird Verwandte und andere vor COVID-19 schützen, Impfstoff wird wieder ein normaleres Berufsleben ermöglichen, Impfstoff wird das Reisen ermöglichen, Impfstoff wird Treffen mit Familie und Freunden ermöglichen, Impfstoff wird Restaurantbesuche und andere Aktivitäten wieder ermöglichen; Wichtigkeit der folgenden Gründe im Hinblick auf die Entscheidung, sich nicht impfen zu lassen: Pandemie wird bald vorbei sein, persönliches Infektionsrisiko ist sehr gering, Risiko durch COVID-19 ist allgemein übertrieben, Sorgen über die Nebenwirkungen von COVID-19-Impfstoffen, Impfstoffe sind noch nicht ausreichend getestet, Impfstoffe sind unwirksam, generelle Ablehnung von Impfungen; Faktoren, die die persönliche Impfbereitschaft erhöhen würden: mehr geimpfte Menschen im Umfeld, viele erfolgreich geimpfte Menschen ohne gravierende Nebenwirkungen, Menschen, die die Impfung empfehlen, sind selbst geimpft, Empfehlung des eigenen Arztes, Entwicklung der Impfstoffe in der Europäischen Union, vollständige Klarheit über Entwicklung, Testung und Zulassung der Impfstoffe, starker Wunsch nach einer Impfung bzw. Befragte/r ist bereits geimpft, keine Impfung geplant; Einstellung zu den folgenden Aussagen zu den Impfstoffen: Vorteile überwiegen mögliche Risiken, in der EU zugelassene Impfstoffe sind sicher, zu schnelle Entwicklung, Testung und Zulassung der Impfstoffe, um sicher zu sein, noch unbekannte potentielle Langzeit-Nebenwirkungen, Impfung ist die einzige Möglichkeit zur Beendigung der Pandemie, kein Verständnis für Impfgegner, Ausrottung ernsthafter Krankheiten durch Impfung; Einstellung zu den folgenden Aussagen: Ansteckung kann auch ohne Impfung vermieden werden, mangelnde Transparenz öffentlicher Behörden in Bezug auf die Corona-Impfstoffe, Impfung gegen COVID-19 ist Bürgerpflicht, Impfung sollte verpflichtend sein, Europäische Union spielt wesentliche Rolle bei der Versorgung des eigenen Landes mit Impfstoff; vertrauenswürdigste Institutionen oder Personen im Hinblick auf die Bereitstellung von Informationen über Corona-Impfstoffe; Interesse an zusätzlichen Informationen über die folgenden Aspekte: Entwicklung, Testung und Zulassung von COVID-19-Impfstoffen, Sicherheit von COVID-19- Impfstoffen, Effektivität von COVID-19-Impfstoffen; Zufriedenheit mit der Handhabung der Impfstrategie durch: nationale Regierung, EU; Anwendbarkeit der folgenden Aussagen: Befragter kennt Menschen mit positivem Corona-Testergebnis, Befragter kennt Menschen mit Corona-Erkrankung, Befragter hatte positives Corona-Testergebnis, Befragter Corona-Erkrankung, Befragter fürchtet Ansteckung in der Zukunft; Impfung des Befragten als: Kind, Erwachsener; Einstellung zu Impfstoffen im allgemeinen: sind sicher, sind wirksam. Demographie: Alter; Geschlecht; Nationalität; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Urbanisierungsgrad; Haushaltszusammensetzung und Haushaltsgröße; Region. Zusätzlich verkodet wurde: Befragten-ID; Land; für das Interview genutztes Gerät; Nationengruppe; Gewichtungsfaktor. Attitudes on vaccination against Covid-19. Topics: preferred time for getting vaccinated; importance of each of the following issues with regard to getting vaccinated: vaccine will help to end the pandemic, vaccine will protect respondent from getting Covid-19, vaccine will protect relatives and others from getting Covid-19, vaccine will make it possible to resume a more normal professional life, vaccine will make it possible to travel, vaccine will make it possible to meet family and friends, vaccine will make it possible to go to restaurants, cinemas etc.; importance of each of the following issues with regard to not getting vaccinated: pandemic will be over soon, personal risk of being infected is very low, risk posed by Covid-19 in general is exaggerated, worries about side effects of Covid-19 vaccines, vaccines have not been sufficiently tested yet, vaccines are ineffective, against vaccines in general; factors to increase personal willingness of getting vaccinated: more people around doing it, more people have already been vaccinated and we see that there are no major side-effects, people that recommend the vaccines are vaccinated themselves, doctor recommends respondent to do so, vaccines are developed in the European Union, full clarity on how vaccines are being developed, tested and authorized, respondent is very eager to get vaccinated or is already vaccinated, won’t get vaccinated anyway; attitude towards the following statements on the vaccines: benefits outweigh possible risks, vaccines authorised in the European Union are safe, vaccines are being developed, tested and authorised too quickly to be safe, vaccines could have long term side-effects that we do not know yet, a vaccine is the only way to end the pandemic, no understanding why people are reluctant to get vaccinated, serious diseases have disappeared thanks to vaccines; attitude towards the following statements: one can avoid being infected without being vaccinated, public authorities are not sufficiently transparent about COVID-19 vaccines, getting vaccinated against COVID-19 is a civic duty, vaccination should be compulsory, European Union is playing a key role in ensuring access to COVID-19 vaccines in the own country; most trustworthy institutions or persons regarding the provision of information about COVID-19 vaccines; interest in additional information about the following aspects: development, testing, and authorization of COVID-19 vaccines, safety of COVID-19 vaccines, effectiveness of COVID-19 vaccines; satisfaction with the handling of the vaccination strategy by: national government, EU; applicability of the following statements: respondent knows people who have tested positive to COVID-19, respondent knows people who have been ill because of COVID-19, respondent has tested positive to COVID-19, respondent has been ill because of COVID-19, respondent fears to be infected in the future; vaccination of respondent: as a child, as an adult; attitude towards vaccines in general: are safe, are effective. Demography: age; sex; nationality; age at end of education; occupation; professional position; type of community; household composition and household size; region. Additionally coded was: respondent ID; country; device used for interview; nation group; weighting factor.

Rotavirus is the most common pathogen causing pediatric diarrhea and an important cause of morbidity and mortality in low- and middle-income countries. Previous evidence suggests that the introduction of rotavirus vaccines in national immunization schedules resulted in dramatic declines in disease burden but may also be changing the rotavirus genetic landscape and driving the emergence of new genotypes. We report genotype data of more than 16,000 rotavirus isolates from 40 countries participating in the Global Rotavirus Surveillance Network. Data from a convenience sample of children under five years of age hospitalized with acute watery diarrhea who tested positive for rotavirus were included. Country results were weighted by their estimated rotavirus disease burden to estimate regional genotype distributions. Globally, the most frequent genotypes identified after weighting were G1P8, G1P6 and G3P8. Genotypes varied across WHO Regions and between countries that had and had not introduced rotavirus vaccine. G1P[8] was less frequent among African (36 vs 20%) and European (33 vs 8%) countries that had introduced rotavirus vaccines as compared to countries that had not introduced. Our results describe differences in the distribution of the most common rotavirus genotypes in children with diarrhea in low- and middle-income countries. G1P[8] was less frequent in countries that had introduced the rotavirus vaccine while different strains are emerging or re-emerging in different regions.