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EWS main components

ML-Based Early Warning System

ML-Based Early Warning System is responsible to provide the necessary notifications (warnings and/or alerts) to the end users in case of potential outbreaks taking into consideration certain rules and patterns based on Machine Learning models. Support users in order to identify needs on material resources, while assist authorities to monitor and validate the effectiveness of policies and measures that are applied.
AIR models architecture (both Single Task Learning and Multi-Task Learning)

Antimicrobial Resistance Model (AIR)

AIR is a Deep Neural Network (DNN) model used for predicting the excess mortality due to antimicrobial resistance. Additionally, it incorporates patient’s contextual epidemiological and infectious information.
WN cycle

BIMS_WN West Nile epidemics model. 

The BIMS_WN model is a muti-agent model whose goal is to predict the occurrence of a WN epidemic using climate and bird migration and movement data.
eu Portfolio of Solutions web site has been initially developed in the scope of DRIVER+ project. Today, the service is managed by AIT Austrian Institute of Technology GmbH., for the benefit of the European Management. PoS is endorsed and supported by the Disaster Competence Network Austria (DCNA) as well as by the STAMINA and TeamAware H2020 projects.