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Reilly Aimee

Argus is an event-based, multi-lingual, biosurveillance system, which captures and analyzes information from publicly available internet media. Argus produces reports that summarize and contextualize direct, indirect, and enviroclimatic indications and warning (I&W) of human, animal, and plant disease events, and makes these reports available to the system’s users. Early warning of highly infectious animal diseases, like foot-and-mouth disease (FMD), is critical for the enactment of containment and/or prevention measures aiming to curb disease spread and reduce the potential for devastating trade and economic implications.

 

Objective

Our objective is to demonstrate how biosurveillance, using direct and indirect I&W of disease within vernacular internet news media, provides early warning and situational awareness for infectious animal diseases that have the potential for trade and economic implications in addition to detecting social disruption. Tracking of I&W during the 2010 Japan FMD epidemic and outbreaks in other Asian countries was selected to illustrate this methodology.

Referenced File
Submitted by hparton on

Argus is an event-based surveillance system which captures information from publicly available Internet media in multiple languages. The information is contextualized and indications and warning (I&W) of disease are identified. Reports are generated by regional experts and are made available to the system's users. In this study a small-scale disease event, plague emergence, was tracked in a rural setting, despite media suppression and a low availability of epidemiological information.

Objective

To demonstrate how event-based biosurveillance can be utilized to closely monitor disease emergence in an isolated rural area, where medical information and epidemiological data are limited, toward identifying areas for public health intervention improvements.

Referenced File
Submitted by elamb on

If the next influenza pandemic emerges in Southeast Asia, the identification of early detection strategies in this region could enable public health officials to respond rapidly. Accurate, real-time influenza surveillance is therefore crucial. Novel approaches to the monitoring of infectious disease, especially respiratory disease, are increasingly under evaluation in an effort to avoid the cost- and timeintensive nature of active surveillance, as well as the processing time lag of traditional passive surveillance. In response to these issues, we have developed an indications and warning (I&W) taxonomy of pandemic influenza based on social disruption indicators reported in news media.

 

Objective

Our aim is to analyze news media for I&W of influenza to determine if the signals they create differ significantly between seasonal and pandemic influenza years.

Referenced File
Submitted by elamb on
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