Optimisation of preparedness and response of health services in major crises using the IMPRESS platform

Nina Dobrinkova*, Thomas Finnie, James Thompson, Ian Hall, Christos Dimopoulos, George Boustras, Yianna Danidou, Nectarios Efstathiou, Chrysostomos Psaroudakis, Nikolaos Koutras, George Eftichidis, Ilias Gkotsis, Marcel Heckel, Andrej Olunczek, Ralf Hedel, Antonis Kostaridis, Marios Moutzouris, Simona Panunzi, Geert Seynaeve, Sofia TsekeridouDanae Vergeti

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


We present the IMPRESS software system; a tool that can support the first responders in cases of disaster management and resources allocation and the optimisation of response to large-scale emergencies. This is a multi-level architecture system that has desktop and mobile interfaces, supporting the decision makers with different modules. Every module has been designed in a way that the data can be unified, no matter the source, and the related calculation engines are providing optimized information for both the users in the incident management centre and on the field. In this article IMPRESS system components are presented, among the validation and optimization activities during the demonstrations implemented in Palermo, Italy (field test exercise), Podgorica, Montenegro (field test exercise) and Sofia, Bulgaria (table top exercise).

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Number of pages20
Publication statusPublished - 2019

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X

Bibliographical note

Funding Information:
Acknowledgements This work has been partially supported by the EC Research Executive Agency 7th Framework Programme, (SEC-2013.4.1-4) under grant number: FP7-SEC-2013-608078-IMproving Preparedness and Response of HEalth Services in major criseS (IMPRESS), the UK NIH Health Protection Research Unit in Emergency Preparedness and Response and the Bulgarian National Science Fund project number DN 12/5 called: Efficient Stochastic Methods and Algorithms for Large-Scale Computational Problems.


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