  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.
