Early Stage Influenza Detection from Twitter
Jiwei Li, Claire Cardie

TL;DR
This paper presents Flu-MN, a Bayesian spatio-temporal model that detects early influenza outbreaks from Twitter data, enabling timely public health responses and reducing disease spread.
Contribution
It introduces Flu-MN, a novel unsupervised Bayesian algorithm leveraging Twitter data for real-time flu detection, improving early outbreak identification over manual systems.
Findings
Effective detection of flu outbreaks in real-time from Twitter data
Outperforms baseline models in early detection accuracy
Builds a practical system aiding health organizations
Abstract
Influenza is an acute respiratory illness that occurs virtually every year and results in substantial disease, death and expense. Detection of Influenza in its earliest stage would facilitate timely action that could reduce the spread of the illness. Existing systems such as CDC and EISS which try to collect diagnosis data, are almost entirely manual, resulting in about two-week delays for clinical data acquisition. Twitter, a popular microblogging service, provides us with a perfect source for early-stage flu detection due to its real- time nature. For example, when a flu breaks out, people that get the flu may post related tweets which enables the detection of the flu breakout promptly. In this paper, we investigate the real-time flu detection problem on Twitter data by proposing Flu Markov Network (Flu-MN): a spatio-temporal unsupervised Bayesian algorithm based on a 4 phase Markov…
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Taxonomy
TopicsData-Driven Disease Surveillance · Influenza Virus Research Studies · Misinformation and Its Impacts
