Early Stage Influenza Detection from Twitter

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 Network, trying to identify the flu breakout at the earliest stage. We test our model on real Twitter datasets from the United States along with baselines in multiple applications, such as real-time flu breakout detection, future epidemic phase prediction, or Influenza-like illness (ILI) physician visits. Experimental results show the robustness and effectiveness of our approach. We build up a real time flu reporting system based on the proposed approach, and we are hopeful that it would help government or health organizations in identifying flu outbreaks and facilitating timely actions to decrease unnecessary mortality.