Worldwide Measles Vaccinations

Tracking measles vaccination rates worldwide

By Throwback Thursday [source]

About this dataset

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

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

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

How to use the dataset

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

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

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

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

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

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

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

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

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

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

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

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

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

• Data Visualization Utilize data visualization techniques such as

Research Ideas

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

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/

Acknowledgements

This dataset is collected by the Duke Global Health Innovation Center.

Each week since November 2020 the Duke Global Health Innovation Centre tracked the unprecedented number of purchases made by countries and multilateral partnerships eager to reserve COVID vaccine supply, many even before any candidates were on the market.

This dataset contains information like the vaccine procurement deals made by countries, the countries economic status, the value of the deal, number of doses procured, the number of people that can be vaccinated, percent of the population that could be covered, and whether the vaccine has got regulatory approval from the government or not.

The dataset can provide interesting insights into a countries' decision-making regarding vaccine procurement. Vaccine procurement deals have been far from equitable with high-income countries purchasing as much as they can while the countries on the other side of the spectrum barely have enough: this could be a nice ground for analyses.

This dataset was last updated on 23rd April 2021.

Notes 1. Country population and economic status figures are from the World Bank
2. EU population is pulled from:
3. COVID Burden Data is based on cumulative COVID-19 cases per million, extracted from https://ourworldindata.org/coronavirus
4. Vaccine labeled as "COVAX Vaccines" is an unspecified vaccine candidate. Covax has purchased 200m doses from SII, will l likely be Oxford/AZ or Novavax. It also includes donation from China
4. 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

Context

As of 1 September 2021, 5.34 billion COVID-19 vaccine doses had been administered worldwide, with 39.6 per cent of the global population having received at least one dose. While 40.5 million vaccines were then being administered daily, only 1.8 per cent of people in low-income countries had received at least a first vaccine by September 2021, according to official reports from national health agencies, which is collated by Our World in Data.

Content

The dataset contains the list of countries, the Number of people who have received at least one dose of a COVID-19 vaccine (unless noted otherwise), and Percentage of population that has received at least one dose of a COVID-19 vaccine.

Acknowledgements

Wikipedai: https://en.wikipedia.org/wiki/Deployment_of_COVID-19_vaccines#cite_note-14

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

COVID-19 vaccination rates slowed in many countries during the second half of 2021, along with the emergence of vocal opposition, particularly to mandated vaccinations. Who are those resisting vaccination? Under what conditions do they change their minds? Our 3-wave representative panel survey from Germany allows us to estimate the dynamics of vaccine opposition, providing the following answers. Without mandates it may be difficult to reach and to sustain the near universal level of repeated vaccinations apparently required to contain the Delta, Omicron and likely subsequent variants. But mandates substantially increase opposition to vaccination. We find that few were opposed to voluntary vaccination in all three waves of the survey. They are just 3.3 percent of our panel, a number that we demonstrate is unlikely to be the result of response error. In contrast, the fraction consistently opposed to enforced vaccinations is 16.5 percent. Under both policies, those consistently opposed and those switching from opposition to supporting vaccination are socio-demographically virtually indistinguishable from other Germans. Thus, the mechanisms accounting for the dynamics of vaccine attitudes may apply generally across societal groups. What differentiates them from others are their beliefs about vaccination effectiveness, trust in public institutions, and whether they perceive enforced vaccination as a restriction on their freedom. We find that changing these beliefs is both possible and necessary to increase vaccine willingness, even in the case of mandates. An inference is that well-designed policies of persuasion and enforcement will be complementary, not alternatives.

This data set provides the data and Stata code used for the article. A detailed description of the variables is available from the corresponding publication. Please cite our paper if you use the data.

The data is in CSV format and includes all historical data on the pandemic up to 03/01/2023, following a 1-line format per country and date.

In the pre-processing of these data, missing data were checked. It was observed, for example, that the missing data referring to new_cases was where the total number of cases had not been changed and that most of the missing data related to vaccination, which actually at the beginning of the pandemic there was no data. Therefore, to solve these cases of missing data it was decided to replace the data containing “NaN” by zero. Some of these features were combined to generate new features. This process that creates new features (data) from existing data, aiming to improve the data before applying machine learning algorithms, is called feature engineering. The new features created were: - Vaccination rate (vaccination_ratio'): total number of people who received at least one dose of vaccine divided by the population at risk. This dose number was chosen because it has a higher correlation with new deaths. - Prevalence: existing cases of the disease at a given time divided by the population at risk of having the disease. Formula: COVID-19 cases ÷ Population at risk * 100. Example: 168,331 ÷ 210,000,000 * 100 = 0.08. - Incidence: new cases of the disease in a defined population during a specific period (one day, for example) divided by the population at risk. Formula: New COVID-19 cases in one day ÷ Population - Total cases * 100. Example: 5,632 ÷ 209,837,301 * 100 = 0.0026.

Our World in Data - COVID-19

▶ About Our World in Data 🏢

• Our World in Data website
• Our World in Data GitHub

▶ Similar Datasets 📄

• COVID-19 World Vaccination Progress
• The Our World in Data COVID Vaccination Data
• Data on COVID-19 (coronavirus)
• COVID-19 dataset by Our World in Data

▶ Context 📝

The complete COVID-19 dataset is a collection of the COVID-19 data maintained and provided by Our World in Data. Our World in Data team will update it daily throughout the duration of the COVID-19 pandemic.

▶ Content 📃

These are the following information that includes in the dataset: | Metrics | Source | Updated | Countries | | --- | --- | | Vaccinations | Official data collated by the Our World in Data team | Daily | 218 | | Tests & positivity | Official data collated by the Our World in Data team | Weekly | 139 | | Hospital & ICU | Official data collated by the Our World in Data team | Weekly | 39 | | Confirmed cases | JHU CSSE COVID-19 Data | Daily | 196 | | Confirmed deaths | JHU CSSE COVID-19 Data | Daily | 196 | | Reproduction rate | Arroyo-Marioli F, Bullano F, Kucinskas S, Rondón-Moreno C | Daily | 185 | | Policy responses | Oxford COVID-19 Government Response Tracker | Daily | 186 | | Other variables of interest | International organizations (UN, World Bank, OECD, IHME…) | Fixed |

Data dictionary is available below ⤵

▶ Acknowledgements 🙏

I'd like to clarify that I'm only making data about vaccines collected by Our World in Data available to Kaggle community. This dataset is gathered, integrated, and posted the new version on a daily basis, as maintained by Our World in Data on their GitHub repository.

▶ Inspiration 💭

• Forecasting daily new confirmed cases of COVID-19 in specific country.
• Perform data analysis/data visualization of COVID-19 cases/death/etc.

📷 Images by Fusion Medical Animation.

The 3.5-million-word Victorian Anti-Vaccination Discourse Corpus (hereon VicVaDis) is intended to provide a (freely accessible) historical resource for the investigation of the earliest public concerns and arguments against vaccination in England, which revolved around compulsory vaccination against smallpox in the second half of the 19th century. It consists of 133 anti-vaccination pamphlets and publications gathered from 1854 to 1906, a span of 53 years that loosely coincides with the Victorian era (1837-1901). This timeframe was chosen to capture the period between the 1853 Vaccination Act, which made smallpox vaccination for babies compulsory, and the 1907 Act which effectively ended the mandatory nature of vaccination.

The Quo VaDis project applies the latest techniques for large-scale computer-aided linguistic analysis to discussions about vaccinations in public discourse, and specifically in: social media discussions in English, UK Parliamentary debates and UK national press reports. The goal is to arrive at a better understanding of pro- and anti-vaccination views, as well as undecided views, which will inform future public health campaigns.

The project will be based in the world-renowned ESRC Centre for Corpus Approaches to Social Science (CASS) at Lancaster University, which was awarded a Queen's Anniversary Prize for Higher and Further Education in 2015. An interdisciplinary project team will work in interaction with three main project partners: Public Health England, the Department of Health and Social Care and the Department for Digital, Culture, Media & Sport.

The World Health Organization's (WHO) list of top ten global health threats includes 'vaccine hesitancy' - 'a delay in acceptance or refusal of vaccines despite availability of vaccination services'. Vaccination programmes are currently estimated to prevent between 2 and 3 million deaths a year worldwide. However, uptake of vaccinations in 90% of countries has been reported to be affected by vaccine hesitancy. In England, coverage for all routine childhood vaccinations is in decline, resulting in the resurgence of communicable diseases that had previously been eradicated. In August 2019, the UK lost its WHO measles elimination status.

The reasons for vaccine hesitancy are complex, but they need to be understood in order to be addressed effectively. This project focuses on discourse because the ways in which controversial topics such as vaccinations are talked about both reflect and shape beliefs and attitudes, which may in turn influence behaviour. More specifically, vaccinations have been the topic of UK parliamentary debates since before the first Vaccination Act of 1840; they have been increasingly discussed in the UK press since the early 1990s; and anti-vaccination views in particular have been described as part of a complex network of 'anti-public discourses' which, in recent years, are known to be both spread and contested on social media.

This project will involve the analysis of three multi-million-word datasets: (1) English-language contributions to three social media platforms: Mumsnet, Reddit and Twitter since the inception of each platform - respectively, 2000, 2005 and 2006; (2) UK national newspapers since 1990; and (3) UK parliamentary debates since 1830. These datasets will be analysed in a data-driven fashion by means of the computer-aided methods associated with Corpus Linguistics - a branch of Linguistics that involves the construction of large digital collections of naturally-occurring texts (known as 'corpora') and their analysis through tailor-made software. A corpus linguistic approach makes it possible to combine in a principled way the quantitative analysis of corpora containing millions of words with the qualitative analysis of individual texts, patterns and interactions. In this way, we will identify and investigate the different ways in which views about vaccinations are expressed in our data, for example, through patterns in choices of vocabulary, pronouns, negation, evaluation, metaphors, narratives, sources of evidence, and argumentation. We will reveal both differences and similarities in pro- and anti-vaccination views over time and across different groups of people, particularly as they form and interact on social media.

Our findings will make a major contribution to an understanding of views about vaccinations both in the UK (via our parliamentary and news datasets) and internationally (via our social media datasets). Through the involvement of our Project Partners, as well as more general engagement activities, these findings will be used as evidence for the design of future public health campaigns about vaccinations.

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...

BackgroundCanine transmitted rabies kills an estimated 59,000 people annually, despite proven methods for elimination through mass dog vaccination. Challenges in directing and monitoring numerous remote vaccination teams across large geographic areas remain a significant barrier to the up-scaling of focal vaccination programmes to sub-national and national level. Smartphone technology (mHealth) is increasingly being used to enhance the coordination and efficiency of public health initiatives in developing countries, however examples of successful scaling beyond pilot implementation are rare. This study describes a smartphone app and website platform, “Mission Rabies App”, used to co-ordinate rabies control activities at project sites in four continents to vaccinate over one million dogs.MethodsMission Rabies App made it possible to not only gather relevant campaign data from the field, but also to direct vaccination teams systematically in near real-time. The display of user-allocated boundaries on Google maps within data collection forms enabled a project manager to define each team’s region of work, assess their output and assign subsequent areas to progressively vaccinate across a geographic area. This ability to monitor work and react to a rapidly changing situation has the potential to improve efficiency and coverage achieved, compared to regular project management structures, as well as enhancing capacity for data review and analysis from remote areas. The ability to plot the location of every vaccine administered facilitated engagement with stakeholders through transparent reporting, and has the potential to motivate politicians to support such activities.ResultsSince the system launched in September 2014, over 1.5 million data entries have been made to record dog vaccinations, rabies education classes and field surveys in 16 countries. Use of the system has increased year-on-year with adoption for mass dog vaccination campaigns at the India state level in Goa and national level in Haiti.ConclusionsInnovative approaches to rapidly scale mass dog vaccination programmes in a sustained and systematic fashion are urgently needed to achieve the WHO, OIE and FAO goal to eliminate canine-transmitted human deaths by 2030. The Mission Rabies App is an mHealth innovation which greatly reduces the logistical and managerial barriers to implementing large scale rabies control activities. Free access to the platform aims to support pilot campaigns to better structure and report on proof-of-concept initiatives, clearly presenting outcomes and opportunities for expansion. The functionalities of the Mission Rabies App may also be beneficial to other infectious disease interventions.

标题：全球儿童疫苗接种政策国家数量统计 数据内容：该数据集统计了全球范围内实施儿童期疫苗接种政策的国家数量。数据内容包括国家名称（Entity）、国家代码（Code）、年份（Year），以及不同疫苗政策（强制性、推荐性）的国家数量统计。具体字段包括： Entity：国家名称 Code：国家代码 Year：统计年份 Countries by vaccine policy (Mandatory for school entry)：实施学校入学前强制疫苗接种政策的国家数量 Countries by vaccine policy (Recommended)：实施推荐疫苗接种政策的国家数量 Countries by vaccine policy (Mandatory)：实施强制疫苗接种政策的国家数量 数据来源：互联网公开数据 数据用途：该数据集可应用于以下领域： 医疗健康：研究全球疫苗接种政策的实施情况

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

Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations.Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555).Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines.Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.

Context

The most awaited COVID-19 Vaccine is out and here is the data of vaccines given in each country.

Content

You can find the number of vaccines provided to the public in each country and analyze.

The file contains 9,921 tweets labelled with the concerns towards vaccines. There are 3 columns in the file: - ID of the tweet in a string format, appended with a "t" (to make it easier to work with on spreadsheet softwares). - The tweet text - The different labels (vaccine concerns) expressed in the tweet, seperated by spaces.

List of the 12 different vaccine concerns in the dataset: - [unnecessary]: The tweet indicates vaccines are unnecessary, or that alternate cures are better. - [mandatory]: Against mandatory vaccination — The tweet suggests that vaccines should not be made mandatory. - [pharma]: Against Big Pharma — The tweet indicates that the Big Pharmaceutical companies are just trying to earn money, or the tweet is against such companies in general because of their history. - [conspiracy]: Deeper Conspiracy — The tweet suggests some deeper conspiracy, and not just that the Big Pharma want to make money (e.g., vaccines are being used to track people, COVID is a hoax) - [political]: Political side of vaccines — The tweet expresses concerns that the governments / politicians are pushing their own agenda though the vaccines. - [country]: Country of origin — The tweet is against some vaccine because of the country where it was developed / manufactured - [rushed]: Untested / Rushed Process — The tweet expresses concerns that the vaccines have not been tested properly or that the published data is not accurate. - [ingredients]: Vaccine Ingredients / technology — The tweet expresses concerns about the ingredients present in the vaccines (eg. fetal cells, chemicals) or the technology used (e.g., mRNA vaccines can change your DNA) - [side-effect]: Side Effects / Deaths — The tweet expresses concerns about the side effects of the vaccines, including deaths caused. - [ineffective]: Vaccine is ineffective — The tweet expresses concerns that the vaccines are not effective enough and are useless. - [religious]: Religious Reasons — The tweet is against vaccines because of religious reasons - [none]: No specific reason stated in the tweet, or some reason other than the given ones.

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...

This dataset contains two files that provide detailed information on Covid-19 deaths and vaccinations worldwide. The first file contains data on the number of Covid-19 deaths, including total deaths and new deaths, across different locations and time periods. The second file contains data on Covid-19 vaccinations, including total vaccinations, people vaccinated, people fully vaccinated, and total boosters, across different locations and time periods. By analyzing this data, you can uncover insights into the global impact of Covid-19 and explore the relationship between vaccinations and deaths. This dataset is a valuable resource for researchers, data analysts, and anyone interested in understanding the ongoing pandemic.

COVID DEATHS - iso_code: The ISO 3166-1 alpha-3 code of the country or territory. - continent: The continent of the location. - location: The name of the country or territory. - date: The date of the observation. - population: The population of the country or territory. - total_cases: The total number of confirmed cases of Covid-19. - new_cases: The number of new confirmed cases of Covid-19. - new_cases_smoothed: The 7-day smoothed average of new confirmed cases of Covid-19. - total_deaths: The total number of deaths due to Covid-19. - new_deaths: The number of new deaths due to Covid-19. - new_deaths_smoothed: The 7-day smoothed average of new deaths due to Covid-19. - total_cases_per_million: The total number of confirmed cases of Covid-19 per million people. - new_cases_per_million: The number of new confirmed cases of Covid-19 per million people. - new_cases_smoothed_per_million: The 7-day smoothed average of new confirmed cases of Covid-19 per million people. - total_deaths_per_million: The total number of deaths due to Covid-19 per million people. - new_deaths_per_million: The number of new deaths due to Covid-19 per million people. - new_deaths_smoothed_per_million: The 7-day smoothed average of new deaths due to Covid-19 per million people. - reproduction_rate: The estimated average number of people each infected person infects (the "R" number). - icu_patients: The number of patients in intensive care units (ICU) with Covid-19 on the given date. - icu_patients_per_million: The number of patients in intensive care units (ICU) with Covid-19 on the given date, per million people. - hosp_patients: The number of patients in hospital with Covid-19 on the given date. - hosp_patients_per_million: The number of patients in hospital with Covid-19 on the given date, per million people. - weekly_icu_admissions: The weekly number of patients admitted to intensive care units (ICU) with Covid-19. - weekly_icu_admissions_per_million: The weekly number of patients admitted to intensive care units (ICU) with Covid-19, per million people. - weekly_hosp_admissions: The weekly number of patients admitted to hospital with Covid-19. - weekly_hosp_admissions_per_million: The weekly number of patients admitted to hospital with Covid-19, per million people.

COVID VACCINATIONS

• total_tests: The total number of tests for Covid-19.
• new_tests: The number of new tests for Covid-19.
• total_tests_per_thousand: The total number of tests for Covid-19 per thousand people.
• new_tests_per_thousand: The number of new tests for Covid-19 per thousand people.
• new_tests_smoothed: The 7-day smoothed average of new tests for Covid-19.
• new_tests_smoothed_per_thousand: The 7-day smoothed average of new tests for Covid-19 per thousand people.
• positive_rate: The share of Covid-19 tests that are positive, given as a rolling 7-day average.
• tests_per_case: The number of tests conducted per confirmed case of Covid-19, given as a rolling 7-day average.
• tests_units: The units used by the location to report its testing data.
• total_vaccinations: The total number of doses of Covid-19 vaccines administered.
• people_vaccinated: The total number of people who have received at least one dose of a Covid-19 vaccine.
• people_fully_vaccinated: The total number of people who have received all doses prescribed by the vaccination protocol.
• total_boosters: The total number of booster doses administered (doses administered after the prescribed number of doses for full vaccination).
• new_vaccinations: The number of doses of Covid-19 vaccines administered on the given date.
• new_vaccinations_smoothed: The 7-day smoothed average of new doses of Covid-19 vaccines administered.
• total_vaccinations_per_hundred: The total number of doses of Covid-19 vaccines administered per hundred people in the total population.
• people_vaccinated_per_hundred: The total number of people who have received at least one dose of a Covid-19 vaccine per hundred people in the total population.
• people_fully_vaccinated_per_hundred: The total number of people who hav...

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 ?

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...

Attitude of participants towards vaccines (n = 500).