• Journal: Scientific Reports
  • Date: Jan. 19, 2017
  • DOI: 10.1038/srep40841
  • Category: Scientific Research


Saurav Ghosh, Prithwish Chakraborty, Elaine O. Nsoesie, Emily Cohn, Sumiko R. Mekaru, John S. Brownstein, and Naren Ramakrishnan created a temporal topic model to investigate how accurately news reports about an outbreak follow the epidemiological dynamics of the outbreak. News interest was quantified and compared to pertussis, salmonella, and E. coli datasets from Project Tycho. The results showed that the temporal topic model was successful and that uniform news coverage can be used to estimate disease case counts with accurate precision.


Saurav Ghosh

Prithwish Chakraborty

Elaine O. Nsoesie

Emily Cohn

Sumiko R. Mekaru

John S. Brownstein

Naren Ramakrishnan

Related Project Tycho Datasets

United States of America - Pertussis

United States of America - Salmonella infection

United States of America - Infection caused by Escherichia coli

United States of America - Infection caused by Shiga toxin producing Escherichia coli

United States of America - Intestinal infection caused by Escherichia coli O157:H7


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 demonstrate that temporal topic trends extracted from disease-related news reports successfully capture 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, when news coverage is uniform, 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.

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