💉 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.

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.

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.

This point layer displays the location of COVID-19 and flu vaccine providers within the province of Manitoba. Vaccination sites are symbolized by their provider type, indicating whether the location is a pharmacy, medical clinic, provincial vaccine clinic, etc. Other information available for each site includes provider name, whether they are accepting appointments, address, and any special notes or restrictions on the location.

This data is populated by Manitoba Health, Seniors and Active Living.

This layer is the data behind the Manitoba COVID-19 & Flu Vaccine Finder application. The data is updated as needed.

Fields included (Alias (Field Name): Field description.)

  Provider Type (Provider_Type): Type of provider. Includes Vaccine Clinic, Pharmacy, Medical Clinic and Urban Indigenous Vaccine Clinic. 
  Provider Name (Provider_Name): Official business name of the provider.
  Status (Status): Current status of location: “Taking appointments” or “Not currently taking appointments”.
  Date (Date): Specific to "Vaccine Clinics" - Date(s) that the location is operating.
  Hours (Hours): Specific to "Vaccine Clinics" - Hours of operation for the location.
  Date Updated (Date_updated): Last date this attribute was updated by the holding provider. 
  Vaccine Comments (Vaccine_Comments): Any limitations, comments or restrictions about the vaccine distribution/eligibility at this location.
  Note (Note): Any additional notes from the provider on their location/requirements. 
  RHA Name (RHA_Name): Name of the Regional Health Authority (RHA) where the site is located.
  Address (Address): Street address or mailing address, if applicable. 
  City (City): City or town in which the provider is located. 
  Province (Province): Province in which the provider is located. The default is "Manitoba". 
  Phone (Phone): Provider's public business phone number.


For more information on Manitoba’s response to COVID-19, please visit the following site: https://www.gov.mb.ca/covid19/index.html