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Publication Detail
Detecting disease outbreaks in mass gatherings using Internet data.
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Journal Article
  • Authors:
    Yom-Tov E, Borsa D, Cox IJ, McKendry RA
  • Publication date:
  • Pagination:
    e154, ?
  • Journal:
    J Med Internet Res
  • Volume:
  • Issue:
  • Status:
    Published online
  • Country:
  • PII:
  • Language:
  • Keywords:
    data mining, infectious disease, infodemiology, information retrieval, mass gatherings, Communicable Diseases, Data Collection, Data Mining, Disease Outbreaks, Humans, Information Seeking Behavior, Internet, Mass Behavior, Music, Public Health Surveillance, Recreation, Saudi Arabia, Search Engine, United Kingdom
BACKGROUND: Mass gatherings, such as music festivals and religious events, pose a health care challenge because of the risk of transmission of communicable diseases. This is exacerbated by the fact that participants disperse soon after the gathering, potentially spreading disease within their communities. The dispersion of participants also poses a challenge for traditional surveillance methods. The ubiquitous use of the Internet may enable the detection of disease outbreaks through analysis of data generated by users during events and shortly thereafter. OBJECTIVE: The intent of the study was to develop algorithms that can alert to possible outbreaks of communicable diseases from Internet data, specifically Twitter and search engine queries. METHODS: We extracted all Twitter postings and queries made to the Bing search engine by users who repeatedly mentioned one of nine major music festivals held in the United Kingdom and one religious event (the Hajj in Mecca) during 2012, for a period of 30 days and after each festival. We analyzed these data using three methods, two of which compared words associated with disease symptoms before and after the time of the festival, and one that compared the frequency of these words with those of other users in the United Kingdom in the days following the festivals. RESULTS: The data comprised, on average, 7.5 million tweets made by 12,163 users, and 32,143 queries made by 1756 users from each festival. Our methods indicated the statistically significant appearance of a disease symptom in two of the nine festivals. For example, cough was detected at higher than expected levels following the Wakestock festival. Statistically significant agreement (chi-square test, P<.01) between methods and across data sources was found where a statistically significant symptom was detected. Anecdotal evidence suggests that symptoms detected are indeed indicative of a disease that some users attributed to being at the festival. CONCLUSIONS: Our work shows the feasibility of creating a public health surveillance system for mass gatherings based on Internet data. The use of multiple data sources and analysis methods was found to be advantageous for rejecting false positives. Further studies are required in order to validate our findings with data from public health authorities.
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