  In recent years social and news media have increasingly been used to explain
patterns in disease activity and progression. Social media data, principally
from the Twitter network, has been shown to correlate well with official
disease case counts. This fact has been exploited to provide advance warning of
outbreak detection, tracking of disease levels and the ability to predict the
likelihood of individuals developing symptoms. In this paper we introduce
DEFENDER, a software system that integrates data from social and news media and
incorporates algorithms for outbreak detection, situational awareness,
syndromic case tracking and forecasting. As part of this system we have
developed a technique for creating a location network for any country or region
based purely on Twitter data. We also present a disease count tracking approach
which leverages counts from multiple symptoms, which was found to improve the
tracking of diseases by 37 percent over a model that used only previous case
data. Finally we attempt to forecast future levels of symptom activity based on
observed user movement on Twitter, finding a moderate gain of 5 percent over a
time series forecasting model.
