Dataset Information

Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks.

ABSTRACT: 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.

SUBMITTER: Ghosh S

PROVIDER: S-EPMC5244405 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks.

Ghosh Saurav S Chakraborty Prithwish P Nsoesie Elaine O EO Cohn Emily E Mekaru Sumiko R SR Brownstein John S JS Ramakrishnan Naren N

Scientific reports 20170119

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 larg ...[more]

PMID: 28102319

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Project description:BACKGROUND: Infectious disease outbreaks in communities can be controlled by early detection and effective prevention measures. Assessing the relative importance of each individual community member with respect to these two processes requires detailed knowledge about the underlying social contact network on which the disease can spread. However, mapping social contact networks is typically too resource-intensive to be a practical possibility for most communities and institutions. METHODS: Here, we describe a simple, low-cost method - called collocation ranking - to assess individual importance for early detection and targeted intervention strategies that are easily implementable in practice. The method is based on knowledge about individual collocation which is readily available in many community settings such as schools, offices, hospitals, and so on. We computationally validate our method in a school setting by comparing the outcome of the method against computational predictions based on outbreak simulations on an empirical high-resolution contact network. We compare collocation ranking to other methods for assessing the epidemiological importance of the members of a population. To this end, we select subpopulations of the school population by applying these assessment methods to the population and adding individuals to the subpopulation according to their individual rank. Then, we assess how suited these subpopulations are for early detection and targeted intervention strategies. RESULTS: Likelihood and timing of infection during an outbreak are important features for early detection and targeted intervention strategies. Subpopulations selected by the collocation ranking method show a substantially higher average infection probability and an earlier onset of symptoms than randomly selected subpopulations. Furthermore, these subpopulations selected by the collocation ranking method were close to the optimum. CONCLUSIONS: Our results indicate that collocation ranking is a highly effective method to assess individual importance, providing critical low-cost information for the development of sentinel surveillance systems and prevention strategies.

Dataset Information

Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks.

Publications

Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks.

Similar Datasets

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