Detecting disease outbreaks in mass gatherings using internet data
- Submitting institution
-
University College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13929
- Type
- D - Journal article
- DOI
-
-
- Title of journal
- Journal of Medical Internet Research
- Article number
- -
- First page
- e154
- Volume
- 16
- Issue
- 6
- ISSN
- 1438-8871
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The risk of transmission of communicable diseases is significantly increased at mass gatherings, as the COVID-19 pandemic has shown. The dispersion of participants after the event poses a challenge for traditional surveillance methods where dispersion can introduce significant latency. This was the first paper to demonstrate the feasibility of creating a real-time public health surveillance system for mass gatherings based on Twitter and Web search logs. Experimental results based on 3 different statistical tests and involving 9 music festivals and the Hajj (with an average of almost 100K Tweets and 16K queries for each) demonstrated the utility of the method.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -