  In retrospective assessments, internet news reports have been shown to
capture early reports of unknown infectious disease transmission prior to
official laboratory confirmation. In general, media interest and reporting
peaks and wanes during the course of an outbreak. In this study, we quantify
the extent to which media interest during infectious disease outbreaks is
indicative of trends of reported incidence. We introduce an approach that uses
supervised temporal topic models to transform large corpora of news articles
into temporal topic trends. The key advantages of this approach include,
applicability to a wide range of diseases, and ability to capture disease
dynamics - including seasonality, abrupt peaks and troughs. We evaluated the
method using data from multiple infectious disease outbreaks reported in the
United States of America (U.S.), China and India. We noted that temporal topic
trends extracted from disease-related news reports successfully captured the
dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue
outbreaks in India (2013) and China (2014). Our observations also suggest that
efficient modeling of temporal topic trends using time-series regression
techniques can estimate disease case counts with increased precision before
official reports by health organizations.
