Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks
This work addresses the need for early detection and monitoring of infectious disease outbreaks for public health officials, though it is incremental as it builds on existing topic modeling and time-series techniques.
The study tackled the problem of assessing whether media interest during infectious disease outbreaks indicates trends in reported incidence, by introducing a supervised temporal topic modeling approach to transform news articles into temporal topic trends, and found that these trends successfully captured outbreak dynamics and could estimate case counts with increased precision before official reports.
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.