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 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.

Demographic summary of interview participants (n = 15).

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.