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.

To facilitate effective targeted COVID-19 vaccination strategies, it is important to understand reasons for vaccine hesitancy where uptake is low. Artificial intelligence (AI) techniques offer an opportunity for real-time analysis of public attitudes, sentiments, and key discussion topics from sources of soft-intelligence, including social media data. In this work, we explore the value of soft-intelligence, leveraged using AI, as an evidence source to support public health research. As a case study, we deployed a natural language processing (NLP) platform to rapidly identify and analyse key barriers to vaccine uptake from a collection of geo-located tweets from London, UK. We developed a search strategy to capture COVID-19 vaccine related tweets, identifying 91,473 tweets between 30 November 2020 and 15 August 2021. The platform's algorithm clustered tweets according to their topic and sentiment, from which we extracted 913 tweets from the top 12 negative sentiment topic clusters. These tweets were extracted for further qualitative analysis. We identified safety concerns; mistrust of government and pharmaceutical companies; and accessibility issues as key barriers limiting vaccine uptake. Our analysis also revealed widespread sharing of vaccine misinformation amongst Twitter users. This study further demonstrates that there is promising utility for using off-the-shelf NLP tools to leverage insights from social media data to support public health research. Future work to examine where this type of work might be integrated as part of a mixed-methods research approach to support local and national decision making is suggested.

💉 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.

Covid-19 misinformation (n = 484).

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.

Perception of the community towards COVID-19 (n = 484).

Demographic summary of interview participants (n = 15).

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