Content

Stock data of the following companies from Oct 2019 is included in this dataset. (BioNTech , Moderna , Johnson & Johnson , Inovio Pharmaceuticals, Sinovac , Sinopharm , Novavax ,Astrazeneca(Oxford)) (The date 2019 was chosen because few companies got IPO just in 2019)

Inspiration

To do more analysis on the performance of the companies with the influence of covid vaccine.

End note

Please let me know if any more companies are to be included or any changes have to be made to improve the quality of the dataset in the discussion section.

Context

The research shows that the U.S. had secured 1.01 billion doses from six different companies up to November 20 which represents the highest quantity of any government apart from India which has made agreements for 1.6 billion. Pfizer/BioNTech and Moderna both account for 100 million U.S. doses each while the U.S. is also set for 500 million doses of the vaccine being developed by the University of Oxford and AstraZeneca.

https://www.statista.com/chart/23660/umber-of-doses-of-covid-19-vaccines-secured-by-the-us/

Content

This chart shows the number of doses of Covid-19 vaccines secured by the U.S. as of November 20, 2020.

Acknowledgements

Niall McCarthy, Data Journalist.

https://www.statista.com/chart/23660/umber-of-doses-of-covid-19-vaccines-secured-by-the-us/

Inspiration

Covid-19 Pandemic.

Owing to the success of linear mRNA coronavirus disease 2019 (COVID-19) vaccines, biopharmaceutical companies and research teams worldwide have attempted to develop more stable circular RNA (circRNA) vaccines and have achieved some preliminary results. This review aims to summarize key findings and important progress made in circRNA research, the in vivo metabolism and biological functions of circRNAs, and research progress and production process of circRNA vaccines. Further, considerations regarding the quality control of circRNA vaccines are highlighted herein, and the main challenges and problem-solving strategies in circRNA vaccine development and quality control are outlined to provide a reference for circRNA vaccine-related research.

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.

Covid-19 misinformation (n = 484).

The COVID Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.

💉 COVID-19 Vaccine Adverse Events (2020-2025): VAERS Real-World Surveillance Data This dataset offers a critical, large-scale look into the real-world safety surveillance of COVID-19 vaccines, sourced from the Vaccine Adverse Event Reporting System (VAERS). Maintained by the CDC and FDA, this collection spans the unprecedented period of mass vaccination from 2020 through 2025, providing an invaluable resource for pharmacovigilance, public health research, and regulatory decision-making.

Key Features & Challenge The dataset is a rich blend of structured and unstructured information detailing reported Adverse Drug Events (ADEs), which range from mild local reactions to severe, life-threatening complications.

Structured Data: Includes standardized symptom codes, offering a direct, quantitative view of reported reactions.

Free-Text Notes: Contains verbose, real-world symptom descriptions provided by reporters. This text is a "treasure trove" of granular context, including details on duration, intensity, and location of symptoms.

The Challenge: The structured entries are limited in scope. The free-text notes, while rich, are inherently noisy and lack standardized metadata such as clinical severity scores or age-specific pattern normalization.

Value to Data Scientists This dataset presents a significant Natural Language Processing (NLP) and Machine Learning (ML) challenge:

Extracting Context: Develop models to effectively extract critical clinical context (e.g., "headache lasting three days, severe") from the raw, non-standardized free-text notes.

Standardizing Severity: Create predictive models to assign standardized severity and age-specific risk patterns to ADEs.

Informed Decision Making: The ultimate goal is to generate actionable, timely insights for regulators, healthcare providers, and pharmaceutical companies, improving both vaccine safety monitoring and public trust.

Dive into this dataset to apply your skills in advanced data cleaning, feature engineering, and state-of-the-art NLP to solve a crucial, high-impact public health challenge.

Context

Pakistan will receive half-a-million free doses of China's Sinopharm COVID-19 vaccine by January 31. I think we make a Covid vaccination distribution plan using this data. we have to find out the shortest route to commute the vaccine to minimize the spread of the COVID-19 and to save the tex pairs money because it needs specially designed,temperature-controlled thermal shippers, utilizing dry ice to maintain recommended storage temperature conditions of -70°C±10°C for up to 10 days unopened.

Content

Pakistan has 6,445 cities, towns, villages and administrative units that are divided among 1872 postal zip codes.

Acknowledgements

This Dataset is got from Pakistan post, Google and https://data.humdata.org/ and few private repos.

Inspiration

if anyone can combine it with other external sources to make it useable for startups and logistics companies to map their supply chain.

Demographic summary of interview participants (n = 15).

Association between demographics and subjects’ perception, vaccine acceptance, and misinformation (n = 484).