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Pandemic crisis prediction and management

Demonstration of intelligent decision support for pandemic crisis prediction and management within and across European borders (STAMINA)

Communicable diseases have the potential to result in serious cross-border public health threats. Efforts have been made to improve health security in the EU area through sharing of information and services. However management of this type of crisis remains an incredible challenge in a cross-border arena where there are different legal, administrative, professional and political cultures and therefore it becomes harder to detect threats, understand current circumstances and make joint decisions.

STAMINA aims to contribute to this crucial effort by focusing on providing solutions for the preparedness and response phases of the emergency management cycle by facilitating intelligent evidence-based decision support for practitioners at national and regional levels involved in pandemic crises management. For this purpose, STAMINA offers a variety of tools and guidelines. STAMINA leverages on concepts and technology that is either commercially available or has sufficiently matured through previous research projects, but not yet used by the national and regional health emergency planners or first responders of Europe in their daily practice of pandemics management.

The Coronavirus pandemic and the way COVID-19 outbreak affects all aspects of our society highlights the need for more research in the area of planning and preparedness for such global crises. STAMINA’s efforts will draw upon evidence and experiences from actions taken in the current pandemic crisis.

Meet the Principal Investigator(s) for the project

Related Research Group(s)


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Computer Science for Social Good - Our group works with partners in the Global South to lead and promote interdisciplinary research in the field of computer science and social good. We focus on investigating and developing new ways and innovative technologies to address challenging socio-economic problems.


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UKRI Research England

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Project last modified 28/03/2022