National Support for Project Medical Ecosystem - Personalized Event-based Surveillance
Reseach leader:Smrž Pavel
Team leaders:Zemčík Pavel
Agency:MŠMT
Code:7E10054
Start:2010
End:2012
Keywords:Public Health Event Detection, Personalization, User
Generated Content
Annotation:
Many factors in today's changing societies contribute towards the continuous emergence of infectious diseases.
Demographic change, globalization, bioterrorism, compounded with the resilient nature of viruses and diseases
such as SARS and avian influenza have raised awareness for European society's increasing vulnerability.
Traditional Epidemic Intelligence systems are designed to identify potential health threats, and rely upon data
transmissions from laboratories or hospitals. They can be used to recognise long-term trends, but are limited
in several ways. Threats, such as SARS, can go unrecognised since the signals indicating its existence may
originate from sources other than the traditional ones. Second, a critical strategy for circumventing devastating
public health events is early detection and early response. Conflictingly, the time with which information
propagates through the traditional channels, can undermine time-sensitive strategies. Finally, traditional systems
are well suited for recognising indicators for known diseases, but are not well designed for detecting those
that are emerging. Faced with these limitations, traditional systems need to be complemented with additional
approaches which are better targeted for the early detection of emerging threats.
The Medical EcoSystem (M-Eco) project, will address these limitations by using Open Access Media and
User Generated Content, as unofficial information sources for Epidemic Intelligence. This type of content has
transformed the manner in which information propagates across the globe. Based on this, M-Eco will develop
an Event-Based Epidemic Intelligence System which integrates unofficial and traditional sources for the early
detection of emerging health threats. M-Eco will emphasize adaptivity and personalized filtering so that relevant
signals can be detected for targeting the needs of public health officials who have to synthesize facts, assess
risks and react to public health threats.

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