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Hartley David

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

Event-based biosurveillance is a practice of monitoring diverse information sources for the detection of events pertaining to human health. Online documents, such as news articles on the Internet, have commonly been the primary information sources in event-based biosurveillance. With the large number of online publications as well as with the language diversity, thorough monitoring of online documents is challenging. Automated document classification is an important step toward efficient event-based biosurveillance. In Project Argus, a biosurveillance program hosted at Georgetown University Medical Center, supervised and unsupervised approaches to document classification are considered for event-based biosurveillance.

 

Objective

This paper describes ongoing efforts in enhancing automated document classification toward efficient event-based biosurveillance. 

Referenced File
Submitted by hparton on

Public health and medical research on mass gatherings (MGs) are emerging disciplines. MGs present surveillance challenges quite different from routine outbreak monitoring, including prompt detection of outbreaks of an unusual disease. Lack of familiarity with a disease can result in a diagnostic delay; that delay can be reduced or eliminated if potential threats are identified in advance and staff is then trained in those areas. Anticipatory surveillance focuses on disease threats in the countries of origin of MG participants. Surveillance of infectious disease (ID) reports in mass media for those locations allows for adequate preparation of local staff in advance of the MG. In this study, we present a novel approach to ID surveillance for MGs: anticipatory surveillance of mass media to provide early reconnaissance information.

 

Objective

To present the value of early media-based surveillance for infectious disease outbreaks during mass gatherings, and enable participants and organizers to anticipate public health threats.

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

Argus is an event-based, multi-lingual surveillance system which captures and analyzes information from publicly available Internet media. Argus produces reports that summarize and contextualize indications and warning (I&W) of emerging threats, and makes these reports available to the system's users. The significance of the Escherichia coli (EHEC) outbreak analyzed here lies primarily in the fact that it raised epidemiological questions and public health infrastructure concerns that have yet to be resolved, and required the development of new resources for detecting and responding to newly-emerging epidemics.

 

Objective

To demonstrate how event-based biosurveillance, using direct and indirect I&W of disease, provides early warning and situational awareness of the emergence of infectious diseases that have the potential to cause social disruption and negatively impact public health infrastructure, trade, and the economy. Specifically, tracking of I&W during the 2011 enterohaemorrhagic EHEC O104:H4 outbreak in Germany and Europe was selected to illustrate this methodology.

Referenced File
Submitted by elamb on

Event-based biosurveillance is a practice of monitoring diverse information sources for the detection of events pertaining to human, plant, and animal health. Online documents, such as news articles, newsletters, and (micro-) blog entries, are primary information sources in it. Document classification is an important step to filter information and machine learning methods have been successfully applied to this task.

 

Objective

The objective of this literature review is to identify current challenges in document classification for event-based biosurveillance and consider the necessary efforts and the research opportunity.

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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