Tracking the flu pandemic by monitoring the social web

V Lampos, Nello Cristianini

Research output: Contribution to journalArticle (Academic Journal)peer-review

253 Citations (Scopus)


Tracking the spread of an epidemic disease like seasonal or pandemic influenza is an important task that can reduce its impact and help authorities plan their response. In particular, early detection and geolocation of an outbreak are important aspects of this monitoring activity. Various methods are routinely employed for this monitoring, such as counting the consultation rates of general practitioners. We report on a monitoring tool to measure the prevalence of disease in a population by analysing the contents of social networking tools, such as Twitter. Our method is based on the analysis of hundreds of thousands of tweets per day, searching for symptom-related statements, and turning statistical information into a flu-score. We have tested it in the United Kingdom for 24 weeks during the H1N1 flu pandemic. We compare our flu-score with data from the Health Protection Agency, obtaining on average a statistically significant linear correlation which is greater than 95%. This method uses completely independent data to that commonly used for these purposes, and can be used at close time intervals, hence providing inexpensive and timely information about the state of an epidemic.
Translated title of the contributionTracking the flu pandemic by monitoring the social web
Original languageEnglish
Pages (from-to)411 - 416
Number of pages6
JournalCognitive Information Processing (CIP), 2010 2nd International Workshop on
Publication statusPublished - 14 Oct 2010

Bibliographical note

ISBN: 9781424464579
Publisher: IEEE Press
Name and Venue of Conference: CIP 2010
Conference Organiser: Fulvio Gini and Sergios Theodoridis


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