Nelson Noele

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.

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.

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.

Hepatitis A virus (HAV) infections have persisted in the United States despite the availability of an effective vaccine. Recent outbreaks of HAV infections among unvaccinated adults attributed to consumption of HAV-contaminated food, or person-to-person contact in certain populations (e.g., men who have sex with men) or settings (e.g., homeless shelters) have emphasized the importance of targeted vaccination of at-risk adults.

Objective:

To evaluate the use of commercial laboratory data for monitoring trends in HAV infections over time and identifying geographic and demographic characteristics of HAV case clusters for the purpose of targeting interventions.