$${\tt surveillance}$$: An R package for the monitoring of infectious diseases
Computational Statistics
Bioinformatics
Unsupervised public health event detection for epidemic intelligence
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A transfer approach to detecting disease reporting events in blog social media
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Applicability of recommender systems to medical surveillance systems
Proceedings of the second international workshop on Web science and information exchange in the medical web
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Disease surveillance systems exist to offer an easily accessible "epidemiological snapshot" on up-to-date summary statistics for numerous infectious diseases. However, these indicator-based systems represent only part of the solution. Experiences show that they fail when confronted with agents that are new emerging like the agents causing the lung disease SARS in 2002. Further, due to slow reporting mechanisms, the time until health threats become visible to public health officials can be long. The M-Eco project provides an event-based approach to the early detection of emerging health threats. The developed technologies exploit content from social media and multimedia data as input and analyze it by sophisticated event-detection techniques to identify potential threats. Alerts for public health threats are provided to the user in a personalized way.