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.

Analysis of ‘COVID vaccination vs. mortality ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sinakaraji/covid-vaccination-vs-death on 12 November 2021.

--- Dataset description provided by original source is as follows ---

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:

--- Original source retains full ownership of the source dataset ---

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

The number of Vaccines received and being administered in different countries. Data from WHO - Africa COVID-19 Dashboard, 2021

This file has a dataset of all African countries that have administered Covid-19 vaccines and the number of people fully vaccinated. It also has a list of the Covax vaccines delivered to the continent.

The COVID-19 pandemic has posed a significant global challenge, with vaccination programs being key to mitigating its impact. Monitoring and analyzing vaccination progress is crucial for understanding the effectiveness of immunization efforts across countries and continents. In order to provide valuable insights into the global vaccination landscape, a Tableau dashboard was developed utilizing the Our World in Data COVID vaccination dataset. This dataset contains up-to-date, official records from governments and health ministries worldwide.

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool Data includes: * Date on which the death occurred * Age group * 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster ##Additional notes As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm. As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category. On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags. The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON. “Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results. Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts. Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different. Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported. Rates for the most recent days are subject to reporting lags All data reflects totals from 8 p.m. the previous day. This dataset is subject to change.

This folder has three files: A table of the types of Covid-19 vaccines and their manufacturers which are fully approved or approved only for emergency use A table of countries that have approved the Covid-19 vaccines A table of doses delivered to countries This data was last updated on 30 June 2022.. For the full methodology see this file here

Context

To be honest it's pretty hard for you to find data on vaccine progress and especially time-based data on a country like Pakistan. So, I created this small but interactive notebook that will keep updating the database until everyone is vaccinated. In this project I have used Pandas for easy WebSracping to get the data from pharmaceutical-technology.com then I have created Sqlite3 database to store the data into three tables. It took me a few tries to get everything working smooth so I started using SQL queries to get the data and then used plotly to plot interactive visualization. I was not sure when they will update the website so, I have created few functions to avoid duplication of data and to inform me on telegram about updates. I have also uploaded the processed data to Kaggle from Deepnote which will be updated daily. At last, I have used the Deepnote Schedule notebook feature to run this notebook every day and successfully publishing the article You can find my work on Deepnote.

Content

• World_Vaccination_Progress.csv -> Countries Vaccination progress
• pakistan_time_series.csv -> Time series data of Pakistan vaccine progress
• world_time_series.csv -> Time series data of World vaccine progress

Columns: - Country :: Names of countries in the world - Doses Administered: Total Doses Administered - Doses per 1000 : Number of Doses per thousand - Fully Vaccinated Population (%) : Percentage of a fully vaccinated person in a country. - Vaccine being used in a country : Types of vaccines used in a country.

For Time-Series

• Date_Time : Timestamp of entry

Acknowledgements

I am thankful for Pharmaceutical Technology for updating the stats on daily basis and publicly provide real-time stats of world's vaccination drive. I also want to thank Deepnote for the introduction of the Schedule notebook feature that has made this automation possible.

Inspiration

The lack of data available in my country drove me to create an automated system that collects data from web. You can read more about it in my article. The second inspiration came from participating in Deepnote competition which was on the data Vaccination drive of your country or World.

Vaccine Strategy The vaccine strategy must meet three public health objectives: — Reducing mortality and serious forms of disease — Protecting caregivers and the care system — Ensuring the safety of vaccines and vaccinations As part of this strategy, places dedicated to vaccination are set up. ### Data The dataset contains a list of places dedicated to COVID-19 vaccination. This list comes from the declarations of the Regional Health Agencies, which consolidate the centres of their regions. The data is extracted every hour. There is not always an update or a fix, only the latest version is available. A new field is added today (centre_type). He comes to tag the center as “Great Center”, “Civil Security”, “USMP”, “USHI”, “USHA”, “Mobile Team”, “SSA”. Attention, in reuses, centres whose field “centre_close” = t (true) are not intended to be exploded to the public. These are centres for specific audiences such as prisons that are present in the list of vaccination centres, but not exposed on www.sante.fr 25/06/21 — A list of fictional vaccination centres has been added (1 per foreign country). They represent all vaccination centres in a country. They are not exposed to the public via sante.fr, it is, of course, not possible to take rdv. The source of the list is that of the Ministry of Foreign Affairs available on data.gouv.fr They have no geographical positions (position 0 0). Their only usefulness is internal to the vaccination data capture system. The GID of these centers is 9 characters and starts with 9900xxxxx

This publication corresponds to the Common Data Model (CDM) specification of the Baseline Use Case proposed in T.5.2 (WP5) in the BY-COVID project on “SARS-CoV-2 Vaccine(s) effectiveness in preventing SARS-CoV-2 infection.” Research Question: “How effective have the SARS-CoV-2 vaccination programmes been in preventing SARS-CoV-2 infections?” Intervention (exposure): COVID-19 vaccine(s) Outcome: SARS-CoV-2 infection Subgroup analysis: Vaccination schedule (type of vaccine) Study Design: An observational retrospective longitudinal study to assess the effectiveness of the SARS-CoV-2 vaccine in preventing SARS-CoV-2 infections using routinely collected social, health and care data from several countries. A causal model was established using Directed Acyclic Graphs (DAGs) to map domain knowledge, theories and assumptions about the causal relationship between exposure and outcome. The DAG developed for the research question of interest is shown below. Cohort definition: All people eligible to be vaccinated (from 5 to 115 years old, included) or with, at least, one dose of a SARS-CoV-2 vaccine (any of the available brands) having or not a previous SARS-CoV-2 infection. Inclusion criteria: All people vaccinated with at least one dose of the COVID-19 vaccine (any available brands) in an area of residence. Any person eligible to be vaccinated (from 5 to 115 years old, included) with a positive diagnosis (irrespective of the type of test) for SARS-CoV-2 infection (COVID-19) during the period of study. Exclusion criteria: People not eligible for the vaccine (from 0 to 4 years old, included) Study period: From the date of the first documented SARS-CoV-2 infection in each country to the most recent date in which data is available at the time of analysis. Roughly from 01-03-2020 to 30-06-2022, depending on the country. Files included in this publication: Causal model (responding to the research question) SARS-CoV-2 vaccine effectiveness causal model v.1.0.0 (HTML) - Interactive report showcasing the structural causal model (DAG) to answer the research question SARS-CoV-2 vaccine effectiveness causal model v.1.0.0 (QMD) - Quarto RMarkdown script to produce the structural causal model Common data model specification (following the causal model) SARS-CoV-2 vaccine effectiveness data model specification (XLXS) - Human-readable version (Excel) SARS-CoV-2 vaccine effectiveness data model specification dataspice (HTML) - Human-readable version (interactive report) SARS-CoV-2 vaccine effectiveness data model specification dataspice (JSON) - Machine-readable version Synthetic dataset (complying with the common data model specifications) SARS-CoV-2 vaccine effectiveness synthetic dataset (CSV) [UTF-8, pipe | separated, N~650,000 registries] SARS-CoV-2 vaccine effectiveness synthetic dataset EDA (HTML) - Interactive report of the exploratory data analysis (EDA) of the synthetic dataset SARS-CoV-2 vaccine effectiveness synthetic dataset EDA (JSON) - Machine-readable version of the exploratory data analysis (EDA) of the synthetic dataset SARS-CoV-2 vaccine effectiveness synthetic dataset generation script (IPYNB) - Jupyter notebook with Python scripting and commenting to generate the synthetic dataset #### Baseline Use Case: SARS-CoV-2 vaccine effectiveness assessment - Common Data Model Specification v.1.1.0 change log #### Updated Causal model to eliminate the consideration of 'vaccination_schedule_cd' as a mediator Adjusted the study period to be consistent with the Study Protocol Updated 'sex_cd' as a required variable Added 'chronic_liver_disease_bl' as a comorbidity at the individual level Updated 'socecon_lvl_cd' at the area level as a recommended variable Added crosswalks for the definition of 'chronic_liver_disease_bl' in a separate sheet Updated the 'vaccination_schedule_cd' reference to the 'Vaccine' node in the updated DAG Updated the description of the 'confirmed_case_dt' and 'previous_infection_dt' variables to clarify the definition and the need for a single registry per person The scripts (software) accompanying the data model specification are offered "as-is" without warranty and disclaiming liability for damages resulting from using it. The software is released under the CC-BY-4.0 licence, which permits you to use the content for almost any purpose (but does not grant you any trademark permissions), so long as you note the license and give credit.

Introduction: In 2021, the European Medicines Agency supported the “Covid Vaccine Monitor (CVM),” an active surveillance project spanning 13 European countries aimed at monitoring the safety of COVID-19 vaccines in general and special populations (i.e., pregnant/breastfeeding women, children/adolescents, immunocompromised people, and people with a history of allergies or previous SARS-CoV-2 infection). Italy participated in this project as a large multidisciplinary network called the “ilmiovaccinoCOVID19 collaborating group.”Methods: The study aimed to describe the experience of the Italian network “ilmiovaccinoCOVID19 collaborating group” in the CVM context from June 2021 to February 2023. Comprising about 30 partners, the network aimed to facilitate vaccinee recruitment. Participants completed baseline and follow-up questionnaires within 48 h from vaccination over a 6-month period. Analyses focused on those who completed both the baseline and the first follow-up questionnaire (Q1), exploring temporal trends, vaccination campaign correlation, and loss to follow-up. Characteristics of recruited vaccinees and vaccinee-reported adverse drug reactions (ADRs) were compared with passive surveillance data in Italy.Results: From June 2021 to November 2022, 22,384,663 first doses and 38,207,452 booster doses of COVID-19 vaccines were administered in Italy. Simultaneously, the study enrolled 1,229 and 2,707 participants for the first and booster doses, respectively. Of these, 829 and 1,879 vaccinees, respectively, completed both baseline and at least Q1 and were included in the analyses, with a significant proportion of them (57.8%/34.3%) belonging to special cohorts. Most vaccinees included in the analyses were women. Comirnaty® (69%) and Spikevax® (29%) were the most frequently administered vaccines. ADR rates following Comirnaty® and Spikevax® were higher after the second dose, particularly following Spikevax®. Serious ADRs were infrequent. Differences were observed in ADR characteristics between CVM and Italian passive surveillance.Conclusion: This study confirmed the favorable safety profile of COVID-19 vaccines, with findings consistent with pivotal clinical trials of COVID-19 vaccines, although different proportions of serious ADRs compared to spontaneous reporting were observed. Continuous evaluation through cohort event monitoring studies provides real-time insights crucial for regulatory responses. Strengthening infrastructure and implementing early monitoring strategies are essential to enhance vaccine safety assessment and prepare for future pandemics.

Context

This dataset provides State-by-state data on United States COVID-19 vaccinations between 20 December of 2020 and 12 January of 2022. Data is taken daily by the United States Centers for Disease Control and Prevention

Columns

us_vaccinations File

- location: State name.

- date: date of the case.

- 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 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.

Data as of: May 18, 2021

Overview: This dataset accompanies a study by the African Translational Research Group conducted during the initial rollout of the COVID-19 vaccine in some sub-Saharan African countries. The study focused on the knowledge, attitudes, and perceptions of sub-Saharan Africans (SSAs), including healthcare workers, regarding the vaccine and its effectiveness. It also examined the critical role of information sources in shaping vaccine hesitancy and resistance.

Conducted from March 14 to May 16, 2021, this dataset offers insights into factors influencing vaccination decisions amidst the pandemic and the spread of misinformation. The study was administered in French and English to 2572 participants aged 18 and over from SSA. Data collected included sociodemographic characteristics (such as age, gender, education, and employment status), medical and vaccination history, and the use of mainstream and social media as information sources during the pandemic.

The study identified three main outcomes: the vaccinated group, which included participants who confirmed being vaccinated against COVID-19; the vaccine hesitancy group, consisting of participants who were 'not sure' or 'no' about being vaccinated but were willing to get vaccinated when available; and the vaccine resistance group, comprising participants who were 'not sure' or 'no' about being vaccinated and were unwilling to get vaccinated even when available. Logistic regression analyses were used to assess the influence of information sources on the likelihood of being vaccinated, hesitant, or resistant. This dataset provides valuable evidence on the knowledge, attitudes, and perceptions of COVID-19, the myths surrounding vaccines, and how different information sources impact vaccination behaviours in SSA. Understanding these dynamics is essential for designing effective communication strategies to combat misinformation and improve vaccine uptake.

This dataset is an invaluable resource for researchers, public health officials, and policymakers aiming to enhance vaccination campaigns and address misinformation challenges in SSA.

Report on Covid-19 vaccinations including Ministry of National Defense from February 10, 2021, demonstrates COVID-19 vaccinations in the first and second dose throughout the country of Cambodia. There are three registered vaccines, namely SinopharmBeijing, COVISHIELD, and Sinovac to be used and allocated to prevent the spread of COVID-19 in the country. The report indicates precisely through each category for volunteers of vaccination enrollment, vaccinated citizens, and non-vaccinated citizens due to health issues in the first and second dose​, along with the available COVID-19 vaccination locations.

This dashboard 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

For each country, the type of COVID-19 vaccine produced, the names of manufacturing facilities, steps of vaccine production and vaccine manufacturing platform are included.

The dataset includes information on the status of immunization campaigns: vaccination services, vaccination campaign type, and target ages of vaccinations impacted by COVID-19 by country from official and unofficial sources.

This dataset provides values for CORONAVIRUS VACCINATION RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This repository contains the dataset used for the paper 'A behaviourally informed chatbot increases vaccination rates in Argentina more than a one-way reminder'. The data originates from administrative databases collected by the Ministry of Health of the Republic of Argentina and the Ministry of Health of the Province of Chaco, Argentina. The original data sources are (1) Nomivac: a complete dataset of all COVID-19 vaccinations across the country; (2) Pasaporte Chaco: the Chaco province's online services phone application; (3) Chaco's 0800 help line: a database from a phone helpline established by the provincial Ministry of Health to address citizens' queries on COVID-19 vaccinations and (4) SUMAR: the Argentinian public subsidised healthcare system. The data have been processed to anonymise identity numbers for public availability and to create variables suitable for regression analysis. Methods This is administrative data collected by Argentina's Ministry of Health. It encompasses four constituent datasets: three phone number databases and Nomivac (briefly explained in the Data Sources section of the Materials and Methods). The data has been anonymized for public availability and processed to generate variables suitable for regression analysis.