The Our World in Data COVID vaccination data

To bring this pandemic to an end, a large share of the world needs to be immune to the virus. The safest way to achieve this is with a vaccine. Vaccines are a technology that humanity has often relied on in the past to bring down the death toll of infectious diseases.

Within less than 12 months after the beginning of the COVID-19 pandemic, several research teams rose to the challenge and developed vaccines that protect from SARS-CoV-2, the virus that causes COVID-19.

Now the challenge is to make these vaccines available to people around the world. It will be key that people in all countries — not just in rich countries — receive the required protection. To track this effort we at Our World in Data are building the international COVID-19 vaccination dataset that we make available on this page.

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...

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.

IntroductionCorrectional facilities are high-priority settings for coordinated public health responses to the COVID-19 pandemic. These facilities are at high risk of disease transmission due to close contacts between people in prison and with the wider community. People in prison are also vulnerable to severe disease given their high burden of co-morbidities.MethodsWe developed a mathematical model to evaluate the effect of various public health interventions, including vaccination, on the mitigation of COVID-19 outbreaks, applying it to prisons in Australia and Canada.ResultsWe found that, in the absence of any intervention, an outbreak would occur and infect almost 100% of people in prison within 20 days of the index case. However, the rapid rollout of vaccines with other non-pharmaceutical interventions would almost eliminate the risk of an outbreak.DiscussionOur study highlights that high vaccination coverage is required for variants with high transmission probability to completely mitigate the outbreak risk in prisons.

Localizing Commitments, Challenges, and Insights on the Road to Immunization Agenda 2030: Responses from 6,185 national and sub-national staff (Immunization Agenda 2030 Full Learning Cycle, 7 March - 20 June 2022)

Abstract

This data set contains survey responses from over 6,000 national and subnational immunization staff who applied to participate in a 2022 Full Learning Cycle (FLC) learning programme of The Geneva Learning Foundation (TGLF), intended to contribute to the Movement for Immunization Agenda 2030. The 95-item questionnaire collected information on respondents' commitment to the movement's principles, demographics, work challenges, motivation and learning culture, and the impact of COVID-19 on routine immunization. The purpose was to understand applicants' priority challenges and readiness to engage in peer learning to advance country and global immunization goals. Questions addressed consent, identity confirmation, COVID-19 vaccination status, employer, role, system level, past participation in the sponsoring organization's programs, work and wellbeing, difficulties with COVID-19 vaccination, outbreak response, gender equity, and reaching zero-dose children. Applicants identified one priority challenge in their work that they would seek to address through the program. This data set offers insights into frontline perspectives on strengthening immunization programs. Secondary analysess were performed in 2022 and 2023 to illuminate human resource issues, gender barriers, pandemic recovery, and peer learning for change.

Research Audience

Education and global health researchers with interest in human resources for health (HRH) and the characteristics, priority challenges, and experiences of national and sub-national immunization staff participating in the Movement for Immunization Agenda (IA) 2030.

Credits

Author

The Geneva Learning Foundation (TGLF) 18 Avenue Louis Casaï CH1209 Geneva, Switzerland research@learning.foundation

Principal Investigator and Corresponding Author

Reda Sadki, TGLF reda@learning.foundation

Project Partners

• Biostat Global Consulting (BGC)
• Bridges to Development
• Centre for Change & Complexity in Learning (C3L)

Partners' Roles and Responsibilities

• Design: TGLF
• Implementation (sample collection): TGLF
• Processing: TGLF, C3L
• Anonymization: BGC
• Data cleaning: Bridges to Development
• Submission: TGLF
• Maintenance of learning analytics database where data are stored: C3L

Funding

Wellcome Trust, Bill & Melinda Gates Foundation (BMGF)

Recommended Citation

The Geneva Learning Foundation, 2023. Full Learning Cycle (2022) Application for national and sub-national immunization staff to identify challenges and join the Movement for Immunization Agenda (IA 2030) (Version 1.0) [Data Set]. The Geneval Learning Foundation. DOI:https://doi.org/10.5281/zenodo.8199552

File List

IA2030_EN_FLC_2022_Application_Survey.README.md (this document)

20220211.IA2030-EN Movement application-FINAL.docx: List of questions included in the questionnaire. (Note: skip patterns are not shown.)

IA2030_EN_Application_Survey_Dataset.xlsx: English version of anonymized Application Survey Dataset. Version 1: Geneva Learning Foundation, 11 August 2023. (6,669 observations; 58 variables)

Related Data Sets

This is a subset of data collected by The Geneva Learning Foundation (TGLF) during the first IA2030 Full Learning Cycle (FLC). The complete IA2030 Application Survey data set is more comprehensive, and includes information such as respondents' gender, employer, professional role, country, and health system level, as well as responses to open-text questions.

Researchers who would like to analyze the full set of unredacted responses are invited to contact the Geneva Learning Foundation to inquire about a Data Sharing Agreement that would stipulate conditions of access (insights@learning.foundation).

The Geneva Learning Foundation, 2023. Value Creation Stories (VCS) weekly feedback survey, 2022 Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) (Version 1.0). [Data Set]. The Geneva Learning Foundation. DOI: https://doi.org/10.5281/zenodo.7763922

Additional data sets for the first Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) are available from TGLF's Insights Unit ().

Survey Purpose

The Immunization Agenda 2030, the global immunization strategy for 2021-2030, set ambitious targets for global immunization coverage and other key indicators (World Health Organization [WHO], 2023a).

In response to the WHO Director-General's call for a social movement to ensure immunization remains a priority for global and regional health agendas and promote broad societal support for immunization (WHO, 2023b), TGLF, working with its global community of over 35,000 alumni, developed a learning programme intended to contribute to a “Movement for Immunization Agenda 2030 (IA2030)”.

In addition to participating in structured peer learning activities, applicants made a pledge to work towards IA2030 and their country's goals, adhere to the IA2030 core principles, and to provide support to their peers making similar commitments.

The purpose of the IA2030 Application Survey was to collect demographic and organizational information from immunization workers who work at the national or sub-national level interested in applying for the 2022 Learning Cycle and for membership in the Movement for IA2030.

Survey Questionnaire

The survey questionnaire consisted of both quantitative (Likert) and qualitative (open-text) responses to 95 questions documenting respondents' commitment to joining the Movement for IA2030 and adhering to relevant principles, demographic characteristics, information about work history and role, work and well-being, learning culture and performance, COVID-19 recovery efforts through vaccination campaigns and routine immunization (including outreach), priority work-related challenges, and most important reason for wanting to join the Movement for IA2030.

Survey content was informed by TGLF's six years of experience working with thousands of immunization workers from over 90 countries.

The survey was administered in English and French. While most of questions were required, several items, including questions about work and well-being and COVID-19 vaccination status, were optional.

Question Scaling and Response Options

Most questions were asked with a 'select one response' instruction, but several encouraged the respondent to 'select all that apply'. - AP_CAR_20 Which of these job categories apply to you? - AP_ENV_55 Where you work, what strategies have been put in place to reach under immunized or zero-dose children? - AP_CHA_63 Is your challenge related to any of these? - AP_ENV_78 What actions are being taken at your level of the health system to strengthen RI or PHC that specifically takes advantage of some aspect of COVID-19 vaccine introduction? - AP_ENV_87 Select all activities used for catch-up. - AP_ENV_91 What were the disruptions related to?

Each person's several responses are stored in a single text variable and separated by commas. Some data management will be necessary to divide these strings of text into individual variables to represent each response option.

Overview of questionnaire for respondents

The following information was shared with all applicants to provide an overview of the questions and their rationale.

First, we ask you for: - Consent to share your data, to confirm your supervisor’s support, and to make commitments to follow country and WHO guidelines on COVID-19 and immunization - Your legal name and birthdate to confirm your identity for certification. - Your WhatsApp number to connect you with other participants in the Movement. - Your organization, role, and health system level, and if you are a TGLF alum. - We ask you about your work and well-being: Before we ask you about the challenges you face, we ask about your work and well-being, especially your motivation and how learning is being supported where you work. - We ask about the challenges you face: In 2020, global immunization coverage levels for infants dropped back to 2009 levels. It is like we lost 11 years of hard work. So we ask you about the challenges you face: COVID-19 vaccination, epidemic outbreaks (measles, yellow fever, etc.), gender barriers, and zero-dose children. - We ask you to pick the challenge that you will work on in the Movement: Then we ask you to identify your most difficult and important challenge. This is the one that you will focus on in the Movement. (You can always change later.) - Are you truly committed to learning with colleagues from all over the world? Because the Movement is about learning, sharing experience, and collaborating with others, we ask you to confirm to what extent this is what you want to do. - We ask you to share your successes, ideas, and lessons learned: Because we know that you have many strengths, we ask you if you want to share a success story, and idea, or a lesson learned with colleagues. - We ask you if you want to help build and shape the Movement for IA2030: We ask you if you want to join the Organizing Committee to help build the Movement for Immunization Agenda 2030. - Global partners request your help: Finally, we ask you to answer questions that IA2030 global partners are specifically interested in, about the effect of COVID-19 on routine immunization, catch-up activities, and your own COVID-19 vaccination. (You can choose to skip these questions.)

Specific questions that respondents were encouraged to reflect upon before writing out their answers

• Tell us more about your work and well-being. What are the worst and best parts of your job? Where do you find the motivation to continue your work? What helps you feel involved in your

Introduction

There are several works based on Natural Language Processing on newspaper reports. Mining opinions from headlines [ 1 ] using Standford NLP and SVM by Rameshbhaiet. Al.compared several algorithms on a small and large dataset. Rubinet. al., in their paper [ 2 ], created a mechanism to differentiate fake news from real ones by building a set of characteristics of news according to their types. The purpose was to contribute to the low resource data available for training machine learning algorithms. Doumitet. al.in [ 3 ] have implemented LDA, a topic modeling approach to study bias present in online news media.

However, there are not many NLP research invested in studying COVID-19. Most applications include classification of chest X-rays and CT-scans to detect presence of pneumonia in lungs [ 4 ], a consequence of the virus. Other research areas include studying the genome sequence of the virus[ 5 ][ 6 ][ 7 ] and replicating its structure to fight and find a vaccine. This research is crucial in battling the pandemic. The few NLP based research publications are sentiment classification of online tweets by Samuel et el [ 8 ] to understand fear persisting in people due to the virus. Similar work has been done using the LSTM network to classify sentiments from online discussion forums by Jelodaret. al.[ 9 ]. NKK dataset is the first study on a comparatively larger dataset of a newspaper report on COVID-19, which contributed to the virus’s awareness to the best of our knowledge.

2 Data-set Introduction

2.1 Data Collection

We accumulated 1000 online newspaper report from United States of America (USA) on COVID-19. The newspaper includes The Washington Post (USA) and StarTribune (USA). We have named it as “Covid-News-USA-NNK”. We also accumulated 50 online newspaper report from Bangladesh on the issue and named it “Covid-News-BD-NNK”. The newspaper includes The Daily Star (BD) and Prothom Alo (BD). All these newspapers are from the top provider and top read in the respective countries. The collection was done manually by 10 human data-collectors of age group 23- with university degrees. This approach was suitable compared to automation to ensure the news were highly relevant to the subject. The newspaper online sites had dynamic content with advertisements in no particular order. Therefore there were high chances of online scrappers to collect inaccurate news reports. One of the challenges while collecting the data is the requirement of subscription. Each newspaper required $1 per subscriptions. Some criteria in collecting the news reports provided as guideline to the human data-collectors were as follows:

The headline must have one or more words directly or indirectly related to COVID-19.

The content of each news must have 5 or more keywords directly or indirectly related to COVID-19.

The genre of the news can be anything as long as it is relevant to the topic. Political, social, economical genres are to be more prioritized.

Avoid taking duplicate reports.

Maintain a time frame for the above mentioned newspapers.

To collect these data we used a google form for USA and BD. We have two human editor to go through each entry to check any spam or troll entry.

2.2 Data Pre-processing and Statistics

Some pre-processing steps performed on the newspaper report dataset are as follows:

Remove hyperlinks.

Remove non-English alphanumeric characters.

Remove stop words.

Lemmatize text.

While more pre-processing could have been applied, we tried to keep the data as much unchanged as possible since changing sentence structures could result us in valuable information loss. While this was done with help of a script, we also assigned same human collectors to cross check for any presence of the above mentioned criteria.

The primary data statistics of the two dataset are shown in Table 1 and 2.

Table 1: Covid-News-USA-NNK data statistics

No of words per headline

7 to 20

No of words per body content

150 to 2100

Table 2: Covid-News-BD-NNK data statistics No of words per headline

10 to 20

No of words per body content

100 to 1500

2.3 Dataset Repository

We used GitHub as our primary data repository in account name NKK^1. Here, we created two repositories USA-NKK^2 and BD-NNK^3. The dataset is available in both CSV and JSON format. We are regularly updating the CSV files and regenerating JSON using a py script. We provided a python script file for essential operation. We welcome all outside collaboration to enrich the dataset.

3 Literature Review

Natural Language Processing (NLP) deals with text (also known as categorical) data in computer science, utilizing numerous diverse methods like one-hot encoding, word embedding, etc., that transform text to machine language, which can be fed to multiple machine learning and deep learning algorithms.

Some well-known applications of NLP includes fraud detection on online media sites[ 10 ], using authorship attribution in fallback authentication systems[ 11 ], intelligent conversational agents or chatbots[ 12 ] and machine translations used by Google Translate[ 13 ]. While these are all downstream tasks, several exciting developments have been made in the algorithm solely for Natural Language Processing tasks. The two most trending ones are BERT[ 14 ], which uses bidirectional encoder-decoder architecture to create the transformer model, that can do near-perfect classification tasks and next-word predictions for next generations, and GPT-3 models released by OpenAI[ 15 ] that can generate texts almost human-like. However, these are all pre-trained models since they carry huge computation cost. Information Extraction is a generalized concept of retrieving information from a dataset. Information extraction from an image could be retrieving vital feature spaces or targeted portions of an image; information extraction from speech could be retrieving information about names, places, etc[ 16 ]. Information extraction in texts could be identifying named entities and locations or essential data. Topic modeling is a sub-task of NLP and also a process of information extraction. It clusters words and phrases of the same context together into groups. Topic modeling is an unsupervised learning method that gives us a brief idea about a set of text. One commonly used topic modeling is Latent Dirichlet Allocation or LDA[17].

Keyword extraction is a process of information extraction and sub-task of NLP to extract essential words and phrases from a text. TextRank [ 18 ] is an efficient keyword extraction technique that uses graphs to calculate the weight of each word and pick the words with more weight to it.

Word clouds are a great visualization technique to understand the overall ’talk of the topic’. The clustered words give us a quick understanding of the content.

4 Our experiments and Result analysis

We used the wordcloud library^4 to create the word clouds. Figure 1 and 3 presents the word cloud of Covid-News-USA- NNK dataset by month from February to May. From the figures 1,2,3, we can point few information:

In February, both the news paper have talked about China and source of the outbreak.

StarTribune emphasized on Minnesota as the most concerned state. In April, it seemed to have been concerned more.

Both the newspaper talked about the virus impacting the economy, i.e, bank, elections, administrations, markets.

Washington Post discussed global issues more than StarTribune.

StarTribune in February mentioned the first precautionary measurement: wearing masks, and the uncontrollable spread of the virus throughout the nation.

While both the newspaper mentioned the outbreak in China in February, the weight of the spread in the United States are more highlighted through out March till May, displaying the critical impact caused by the virus.

We used a script to extract all numbers related to certain keywords like ’Deaths’, ’Infected’, ’Died’ , ’Infections’, ’Quarantined’, Lock-down’, ’Diagnosed’ etc from the news reports and created a number of cases for both the newspaper. Figure 4 shows the statistics of this series. From this extraction technique, we can observe that April was the peak month for the covid cases as it gradually rose from February. Both the newspaper clearly shows us that the rise in covid cases from February to March was slower than the rise from March to April. This is an important indicator of possible recklessness in preparations to battle the virus. However, the steep fall from April to May also shows the positive response against the attack. We used Vader Sentiment Analysis to extract sentiment of the headlines and the body. On average, the sentiments were from -0.5 to -0.9. Vader Sentiment scale ranges from -1(highly negative to 1(highly positive). There were some cases

where the sentiment scores of the headline and body contradicted each other,i.e., the sentiment of the headline was negative but the sentiment of the body was slightly positive. Overall, sentiment analysis can assist us sort the most concerning (most negative) news from the positive ones, from which we can learn more about the indicators related to COVID-19 and the serious impact caused by it. Moreover, sentiment analysis can also provide us information about how a state or country is reacting to the pandemic. We used PageRank algorithm to extract keywords from headlines as well as the body content. PageRank efficiently highlights important relevant keywords in the text. Some frequently occurring important keywords extracted from both the datasets are: ’China’, Government’, ’Masks’, ’Economy’, ’Crisis’, ’Theft’ , ’Stock market’ , ’Jobs’ , ’Election’, ’Missteps’, ’Health’, ’Response’. Keywords extraction acts as a filter allowing quick searches for indicators in case of locating situations of the economy,

Abstract: We examine the implications of the very low competitiveness of the Brazilian vaccine RD&I system, which precludes the development of all the important vaccines required by the National Immunization Program (NIP), severely impacting the healthcare of the population. In a country dramatically affected by COVID-19 pandemic and by an exponential increase in emerging and neglected diseases, particularly the poor, these RD&I constraints for vaccines become crucial governance issues. Such constraints are aggravated by a global scenario of limited commercial interest from multinational companies in vaccines for neglected and emerging diseases, which are falling into a “valley of death,” with only two vaccines produced in a pipeline of 240 vaccines. We stress that these constraints in the global pipeline are a window of opportunity for vaccine manufacturers in Brazil and other developing countries in the current paradigm transition towards Vaccinology 4.0. We conclude with recommendations for a new governance strategy supporting Brazilian public vaccine manufacturers in international collaborations for a sustainable national vaccine development and production plan by 2030.

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.

Background: Routine childhood immunization is the most cost-effective method to prevent infection and decrease childhood morbidity and mortality. The COVID-19 pandemic has affected access to health care in Saudi Arabia, including mandatory vaccinations for young children. We aimed to assess the prevalence of intentionally delayed vaccinations in children aged ≤ 2 years during the COVID-19 pandemic curfew in Saudi Arabia, its relation to the caregivers' fear of infection, and identifying factors affecting the caregivers' decision.Methods: We conducted a cross-sectional study using a self-administered survey that targeted primary caregivers of children aged ≤ 2 years residing in Saudi Arabia during the COVID-19 pandemic curfew (March 4–July 6, 2020).Results: We received responses from 577 caregivers, of whom 90.8% were mothers. The prevalence of intentional vaccination delay was 37%. Upon adjusting the potential confounders, the odds of delaying scheduled childhood vaccination because of COVID-19 pandemic fears were greater among caregivers with higher levels of fear (OR 1.10, 95% CI 1.02–1.11). Common reasons for delaying vaccinations were COVID-19 infection and prevention of exposure to COVID-19 cases.Conclusion: Intentional vaccination delay leaves young children vulnerable to preventable infectious diseases. Identifying these children and offering catch-up vaccinations reduces this risk. Campaigns to increase awareness about the dangers of delaying vaccine-preventable diseases must be promoted to caregivers in addition to the promotion of home vaccination services. In preparation for future pandemics, we recommend countries consider interventions to control the level of fear and anxiety provoked by the pandemics and media, and interventions for improved access to vaccinations.

In the Corona pandemic, especially in the phase before vaccines were available, people's risk of infection with COVID-19 was dependent on the adherence to pandemic behaviors (e. g., wearing masks) of others around them. To explore whether altruistic individuals are more likely to engage in pro-social behaviors to protect others during the pandemic, we use data from the European COVID Survey (ECOS). The data was collected in September 2020 and consisted of a representative sample from seven European countries (N = 7,025). Altruism was measured as a deviation from purely self-interested behavior by asking respondents how much they would be willing to donate from an unexpected gain to the equivalent of 1000€. Respondents who were willing to donate more than 0 Euros (68.7%) were treated as altruistic; on average, respondents were willing to donate 11.7% (SD 17.9) of the gain. Controlling for country, sociodemographics, general risk aversion and COVID-specific risk aversion, we find that individuals classified as altruistic were more likely to behave pro-socially. More specifically, we find that altruistic respondents were more likely to wait at home for test results and wear a mask where it is recommended. They would also stay about 1 day longer under quarantine without symptoms after visiting a high-risk country and were less likely to go to a supermarket with COVID symptoms. We find no significant effect for wearing a mask in places where it is mandatory and for inviting more than six people into the house. Furthermore, we find that the subjective risk assessment of COVID-19 also plays a role in these behaviors. Our results support evidence from the literature that suggests that adherence to pro-social pandemic behaviors may be increased if public health officials emphasize the altruistic nature of these behaviors.

BackgroundA vaccine against COVID-19 is a vital tool in managing the current pandemic. It is becoming evident that an effective vaccine would be required to control COVID-19. Effective use of vaccines is very important in controlling pandemics and paving the way for an acceptable exit strategy. Therefore, this systematic review and meta-analysis aims to determine the global COVID-19 acceptance rate that is necessary for better management of COVID-19 pandemic.MethodsThis review was conducted based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocols and considered the studies conducted on acceptance and/or hesitancy of COVID-19 vaccine. Articles were searched using electronic databases including PubMed, Scopus, Web of Science, Embase, CINAHL, and Google Scholar. The quality of the study was assessed using the Joanna Briggs Institute (JBI) critical assessment tool to determine the relevance of each included article to the study.ResultsOf the 6,021 articles identified through the electronic database search, 68 articles were included in the systematic review and meta-analysis. The global pooled acceptance rate of the COVID-19 vaccine was found to be 64.9% [95% CI of 60.5 to 69.0%]. Based on the subgroup analysis of COVID-19 vaccine acceptance rate by the World Health Organization's region, the countries where the study was conducted, occupation, and survey period, the prevalence of COVID-19 vaccine acceptance rate was 60.8% [95% CI: 56.3, 65.2%], 61.9% [95% CI: 61.3, 62.4%], 81.6% [95% CI: 79.7, 83, 2%] and 64.5% [95% CI: 60.3, 68.5%], respectively.ConclusionsThis review revealed the variation in the level of COVID-19 vaccine acceptance rate across the world. The study found that the overall prevalence of COVID-19 vaccine acceptance was 64.9%. This finding indicated that even if the COVID-19 vaccine is developed, the issue of accepting or taking the developed vaccine and managing the pandemic may be difficult.

BackgroundCOVID-19 vaccines are required for individuals with myasthenia gravis (MG), as these patients are more likely to experience severe pneumonia, myasthenia crises, and higher mortality rate. However, direct data on the safety of COVID-19 vaccines in patients with MG are lacking, which results in hesitation in vaccination. This scoping was conducted to collect and summarize the existing evidence on this issue.MethodsPubMed, Cochrane Library, and Web of Science were searched for studies using inclusion and exclusion criteria. Article titles, authors, study designs, demographics of patients, vaccination information, adverse events (AEs), significant findings, and conclusions of included studies were recorded and summarized.ResultsTwenty-nine studies conducted in 16 different countries in 2021 and 2022 were included. Study designs included case report, case series, cohort study, cross-sectional study, survey-based study, chart review, and systemic review. A total of 1347 patients were included. The vaccines used included BNT162b2, mRNA-1273, ChAdOx1 nCoV-19, inactivated vaccines, and recombinant subunit vaccines. Fifteen case studies included 48 patients reported that 23 experienced new-onset, and five patients experienced flare of symptoms. Eleven other types of studies included 1299 patients reported that nine patients experienced new-onset, and 60 participants experienced flare of symptoms. Common AEs included local pain, fatigue, asthenia, cephalalgia, fever, and myalgia. Most patients responded well to treatment without severe sequelae. Evidence gaps include limited strength of study designs, type and dose of vaccines varied, inconsistent window of risk and exacerbation criteria, limited number of participants, and lack of efficacy evaluation.ConclusionCOVID-19 vaccines may cause new-onset or worsening of MG in a small proportion of population. Large-scale, multicenter, prospective, and rigorous studies are required to verify their safety.

IntroductionUntil vaccines became available in late 2020, our ability to prevent the spread of COVID-19 within countries depended largely on voluntary adherence to mitigation measures. However, individual decision-making regarding acceptable COVID-19 risk is complex. To better understand decision-making regarding COVID-19 risk, we conducted a qualitative substudy within a larger Berkeley COVID-19 Safe Campus Initiative (BCSCI) during the summer of 2020, and completed a mixed-methods analysis of factors influencing decision-making.Materials and methodsWe interviewed 20 participants who tested positive for SARS-CoV-2 and 10 who remained negative, and analyzed quantitative survey data from 3,324 BCSCI participants. The BCSCI study enrolled university-affiliated people living in the local area during summer of 2020, collected data on behaviors and attitudes toward COVID-19, and conducted SARS-CoV-2 testing at baseline and endline.ResultsAt baseline, 1362 students (57.5%) and 285 non-students (35.1%) said it had been somewhat or very difficult to comply with COVID-19-related mandates. Most-cited reasons were the need to go out for food/essentials, difficulty of being away from family/friends, and loneliness. Eight interviewees explicitly noted they made decisions partially because of others who may be at high risk. We did not find significant differences between the behaviors of students and non-students.DiscussionDespite prevailing attitudes about irresponsibility of college students during the COVID-19 pandemic, students in our study demonstrated a commitment to making rational choices about risk behavior, not unlike non-students around them. Decision-making was driven by perceived susceptibility to severe disease, need for social interaction, and concern about risk to others. A harm reduction public health approach may be beneficial.

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

Baseline characteristics enrolled patients in a randomized trial of self-administered pulse oximetry among COVID-19 patients in Honduras.