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Detecting disease outbreaks in mass gatherings using Internet data.

Elad Yom-Tov1, Diana Borsa, Ingemar J Cox

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Journal of Medical Internet Research
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Internet data analysis can detect disease outbreaks at mass gatherings. This study used Twitter and search engine queries to identify potential public health risks following large events.

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Area of Science:

  • Public Health Surveillance
  • Computational Epidemiology
  • Digital Health

Background:

  • Mass gatherings pose a significant risk for communicable disease transmission.
  • Participant dispersal after events complicates traditional disease surveillance.
  • Internet data offers a novel approach for real-time outbreak detection.

Purpose of the Study:

  • To develop algorithms for detecting communicable disease outbreaks using Internet data.
  • To leverage social media (Twitter) and search engine queries for public health surveillance.
  • To create an early warning system for potential disease spread from mass gatherings.

Main Methods:

  • Collected Twitter posts and Bing search queries related to nine UK music festivals and the Hajj in 2012.
  • Analyzed data for disease symptom-associated keywords before and after events.
  • Compared keyword frequencies against national data to identify significant increases.

Main Results:

  • Analysis of millions of tweets and thousands of search queries from event attendees.
  • Statistically significant detection of disease symptoms (e.g., cough) following two festivals.
  • High agreement between different analytical methods and data sources (Twitter, search queries).

Conclusions:

  • Demonstrated the feasibility of an Internet-data-driven public health surveillance system for mass gatherings.
  • Utilizing multiple data sources and analysis methods improves accuracy and reduces false positives.
  • Further validation with public health authority data is recommended to confirm findings.