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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.
Physical Activity Lifelong Modelling & Simulation (PALMS) model
The Physical Activity Lifelong Modelling & Simulation (PALMS) model is an agent-based micro-simulation that predicts the lifelong physical activity behaviour of individuals of a population and its effect on their quality of life.
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.
SARS-Cov-2 and Escherichia coli (E. coli) ESBL detection with SHERLOCK/DETECTR
Rapid detection of SARS-Cov-2 and E. coli (ESBL) via the utilisation of a portable SHERLOCK/DETECTR approach.
Real-time detection of Measles, West Nile Virus and SARS-Cov-2
Genetic markers allowing real time point-of-service detection of Measles, West Nile Virus and SARS-Cov-2 via the utilization of a portable qcLAMP device
LifeX COP is a web-centric multi-user Solution developed by Frequentis to address the lack of a Common Operational Picture in the field of Crisis Management.
Contrail Flood Monitoring
Real-time Flood Early Warning System
COVID-19 Information and Symptom Checker chatbot
If people know the facts and check their symptoms, they can be guided on when it’s necessary to isolate themselves, visit health facilities, or continue to follow recommended practices to slow the spread of the disease.
|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.|