About

Community collected, cleaned and organized COVID-19 datasets about India sourced from different government websites which are freely available to all. Here we have digitized them, so it can be used by all the researchers and students.

Infected Data

Column Discription

Main file in this dataset is COVID-19_India_Data.csv and the detailed descriptions are below.

Date_reported : Date of the observation in YYYY-MM-DD
cum_cases : Cumulative number of confirmed cases till that date
cum_death : Cumulative number of deaths till that date
cum_recovered : Cumulative number of recovered patients till that date
new_recovered : Daily new recovery
new_cases : New confirmed cases. Calculated by: current cum_cases - previous cum_case
new_death : New confirmed deaths. Calculated by: current cum_death - previous cum_death
cum_active_cases : Cumulative number of infected person till that date. Calculated by: cum_cases - cum_death - cum_recovered

Vaccination data

Column Discription

Main file in this dataset is Vaccination.csv and the detailed descriptions are below.

• date: date of the observation.
• total_vaccinations: total number of doses administered. For vaccines that require multiple doses, each individual dose is counted. 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. If they receive a third/booster dose, it goes up by again.
• 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_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.
• 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.
• total_vaccinations_per_hundred: total_vaccinations per 100 people in the total population of the country.
• people_vaccinated_per_hundred: people_vaccinated per 100 people in the total population of the country.

Acknowledgements

• Johns Hopkins University for making the data available for educational and academic research purposes.
• World Health Organization (WHO): https://www.who.int/
• Government of India: https://www.mygov.in/covid-19, Ministry of Health and Family Welfare: https://www.mohfw.gov.in/
• Our World in Data: https://ourworldindata.org/covid-vaccinations?country=~IND
• https://data.covid19india.org/

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

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.

Project Overview This portion of the COVID DIARIES project provides full bibliographic information (including original and permanent links) to media items related to the COVID-19 vaccination program, published on the official websites of 20 major U.S. news outlets, including television networks, magazines, and newspapers. It spans the period from December 2020, when states began implementing Phase 1a of the vaccine allocation plan, through September 2021, when vaccines became widely available to all adults and were frequently mandated. News items were collected to preserve a contemporaneous record of how the vaccination effort was discussed across national media. The dataset enables researchers to analyze media communication strategies during a nationwide public health emergency, with the broader aim of informing more effective public health messaging through mass media. This project represents a collaborative effort between the Yale School of Medicine and the Tobin Center for Economic Policy. Data and Data Collection Overview This collection comprises 5,383 unique publication links from 20 major news outlets—including television networks, magazines, and newspapers—published between December 1, 2020, and September 30, 2021. Only articles that were freely accessible online without subscription or paywall restrictions were included. Articles were collected by the research team (specifically AM) between August 2021 and November 2023 and in April 2024 (by AM and AG). These 20 news outlets were selected based on a 2020–2021 survey of 511 U.S. adults, which identified the outlets most commonly used to obtain information about the COVID-19 vaccination program. A full list of news outlets, along with their reported usage and perceived trustworthiness, is provided in Sources_Selection.docx. Online publications were identified using Google search with a custom date range in week-long increments (e.g., 12/01/2020–12/07/2020), using the keyword “vaccine” in combination with the link to the respective news outlet’s website. Search results were manually reviewed by AM according to the following inclusion and exclusion criteria. Inclusion criteria: Articles published on the selected U.S. news outlets websites ending in “.com” or “.co” that relate to the COVID-19 vaccination program; Articles from the selected international news outlets that serve both their country of origin and the U.S. audience (e.g., BBC, The Daily Mail). Exclusion criteria: Articles published on the international news outlets websites that exclusively serve their country of origin (e.g., domains ending in .uk, .ca, etc. without .com, .co); Publications from universities, government agencies, or other organizations not affiliated with major U.S. news outlets (e.g., domains ending in .edu, .gov, .org); Videos without accompanying transcripts; Publications without textual content; Articles referencing vaccines unrelated to COVID-19; Non-English language publications. Selection and Organization of Shared Data The full list of publications is provided in the data file named "News_Outlets_Publications_Full_List." Entries are organized by news outlet (one per tab), then by publication year, month, week, and article title within each tab. For each entry, the list includes the article’s original download date by the research team, file format (e.g., PDF), original link to the publication, and a permanent link record. The list was verified by MC, CA, AV, AG, and AM, with final quality control performed by AM. Each article was assigned a unique identifier in the format: "Article Title – News Outlet Name", ensuring that each entry appears only once in the final dataset. Additional documentation includes this Data Narrative, a document explaining the source selection and an administrative README file.

Coronavirus Vaccination Data

43.5% of the world population has received at least one dose of a COVID-19 vaccine. 5.98 billion doses have been administered globally, and 28.8 million are now administered each day. Only 2% of people in low-income countries have received at least one dose.

Vaccinations

Acknowledgements

The mission is to make data and research on the world's largest problems understandable and accessible.

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.

Prepared by Lan Thuong Nguyen, a PhD. Candidate from the International Doctoral Program in Asia-Pacific Studies (IDAS) at National Chengchi University (NCCU), at the Center for Asia-Pacific Resilience and Innovation (CAPRi).

Lan Thuong Nguyen is a co-author of this project alongside an American researcher, Dr. Yen Pottinger, who has clearly defined responsibilities. Her role is sourcing and analyzing documents related to public health policies during the COVID-19 pandemic, vaccination promotion programs, communication strategies against COVID-19, and research articles and reports on vaccine acceptance rates among the Vietnamese population. Additionally, she examines public sentiment regarding the government's COVID-19 strategies and other relevant information. As a result, she searched, curated, and compiled the datasets and stored them in the depositar. She is also responsible for overseeing the storage, management, and, if necessary, customization of these data. The management process does not require additional resources or incur storage or data preparation costs. The datasets will be shared via the repository, with access requests managed by Lan Thuong Nguyen. No personal data is included in the datasets.

The project titled "Misinformation, Disinformation, and Vaccine Hesitancy in Vietnam" forms part of a broader series of studies analyzing vaccine hesitancy across various countries in the Asia-Pacific region. This research examines both the historical context and the impact of the COVID-19 pandemic, with a particular focus on the influence of misinformation and disinformation on governmental and civil society efforts to promote vaccination. It belongs to the Center for Asia-Pacific Resilience and Innovation (CAPRi). The project has been completed and posted on the Center for Asia-Pacific Resilience and Innovation (CAPRi) website.

In this case, the project aims to analyze the factors contributing to vaccine hesitancy in Vietnam, with a particular focus on the influence of misinformation and disinformation. It will examine the historical context, the role of digital and social media, and the effectiveness of governmental and public health responses in addressing these challenges during the COVID-19 pandemic. The project contains metadata on the Vietnamese vaccination program and focuses on the country's public health policy, communication strategies, and vaccination experiences.

The dataset below is part of this project. It introduces the COVID-19 prevention policies, provides an overview of the current status, and compiles academic research on vaccine acceptance, the prevalence of misinformation, and how governments are addressing these issues.

Files must be downloaded to use the entire dataset (depositar only provides limited data previews). This dataset comprises one ZIP file, one XLSX spreadsheet, and one PDF file. The ZIP files contain academic research and documents on experiences propagating COVID-19 vaccination in Vietnamese and English. They are collected for reference in this project, and each article/ research paper/ report is attached with links in this ZIP file. The XLSX spreadsheet is a collection of public health policies applicable to the country made by the author to understand how the Vietnamese government prevented, combated, and governed the anti-COVID-19 campaign. It is used for reference purposes. The PDF file is a literature review written by the author with detailed citations and references. It is conducted based on the requirements of the project manager to have an overview of Vietnam's public health policy.

In its present state, the dataset is presented primarily in Vietnamese and English.

Content

The WHO coronavirus (COVID-19) dashboard presents official daily counts of COVID-19 cases, deaths and vaccine utilization reported by countries, territories and areas. Through this dashboard, we aim to provide a frequently updated data visualization, data dissemination and data exploration resource, while linking users to other useful and informative resources.

Caution must be taken when interpreting all data presented, and differences between information products published by WHO, national public health authorities, and other sources using different inclusion criteria and different data cut-off times are to be expected. While steps are taken to ensure accuracy and reliability, all data are subject to continuous verification and change. All counts are subject to variations in case detection, definitions, laboratory testing, vaccination strategy, and reporting strategies.

Acknowledgements

© World Health Organization 2020, All rights reserved.

WHO supports open access to the published output of its activities as a fundamental part of its mission and a public benefit to be encouraged wherever possible. Permission from WHO is not required for the use of the WHO coronavirus disease (COVID-19) dashboard material or data available for download. It is important to note that:

WHO publications cannot be used to promote or endorse products, services or any specific organization.

WHO logo cannot be used without written authorization from WHO.

WHO provides no warranty of any kind, either expressed or implied. In no event shall WHO be liable for damages arising from the use of WHO publications.

For further information, please visit WHO Copyright, Licencing and Permissions.

Citation: WHO COVID-19 Dashboard. Geneva: World Health Organization, 2020. Available online: https://covid19.who.int/

Inspiration

Daily cases start increasing suddenly just before the new year and there's a fear for the upcoming wave. Everybody starts to predict the peak cases in the 3rd wave and the date the peak will be reached. Assume you are in the 1st week of January 2022 and there's panic in the country, for the Omicron variant is said to be highly transmittable. Using your machine learning and deep learning skills, you have to create a model that predicts accurately the peak for the 3rd wave.

Multivariate logistic regression analysis of factors affecting decision of vaccinating children against COVID-19 (n = 500).

Attitude of participants towards vaccines (n = 500).