A roadmap to integrated digital public health surveillance: The vision and the challenges

The exponentially increasing stream of real time big data produced by Web 2.0 Internet and mobile networks created radically new interdisciplinary challenges for public health and computer science. Traditional public health disease surveillance systems have to utilize the potential created by new situationaware realtime signals from social media, mobile/sensor networks and citizens' participatory surveillance systems providing invaluable free realtime event-based signals for epidemic intelligence. However, rather than improving existing isolated systems, an integrated solution bringing together existing epidemic intelligence systems scanning news media (e.g., GPHIN, MedISys) with real-time social media intelligence (e.g., Twitter, participatory systems) is required to substantially improve and automate early warning, outbreak detection and preparedness operations. However, automatic monitoring and novel verification methods for these multichannel event-based real time signals has to be integrated with traditional case-based surveillance systems from microbiological laboratories and clinical reporting. Finally, the system needs effectively support coordination of epidemiological teams, risk communication with citizens and implementation of prevention measures. However, from computational perspective, signal detection, analysis and verification of very high noise realtime big data provide a number of interdisciplinary challenges for computer science. Novel approaches integrating current systems into a digital public health dashboard can enhance signal verification methods and automate the processes assisting public health experts in providing better informed and more timely response. In this paper, we describe the roadmap to such a system, components of an integrated public health surveillance services and computing challenges to be resolved to create an integrated real world solution

MDL-based Models for Transliteration Generation

Peer reviewe

Multilingual Real-Time Event Extraction for Border Security Intelligence Gathering.

This chapter gives an overview of tools developed for Fron- tex, the European Agency for the Management of Operational Cooper- ation at the External Borders of the Member States of the European Union, to facilitate the process of extracting structured information on events related to border security from on-line news articles, with a partic- ular focus on incidents and developments in the context of illegal migra- tion, cross-border crime, and related crisis situations at the EU external borders and in third countries. A hybrid event extraction system has been constructed, which consists of two core event extraction engines, namely, NEXUS, developed at the Joint Research Centre (JRC) of the European Commission and PULS, developed at the University of Helsinki. These systems are applied to the stream of news articles continuously gathered and pre-processed by the Europe Media Monitor (EMM)—a large-scale multilingual news aggregation engine, developed at the JRC. In order to bridge the automated analysis phase with in-depth human analysis phase an event moderation tool has been developed, which allows the user to access the database of automatically extracted event descriptions and to clean, validate, group, enhance and export them into other knowledge repositories.JRC.G.2-Global security and crisis managemen