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

Project Information

STAMINA

Grant agreement ID: 883441

DOI

10.3030/883441

Project closed

EC signature date 22 April 2020

Start date 1 September 2020

End date 28 February 2023

Funded under

• Secure societies - Protecting freedom and security of Europe and its citizens

Total cost

€ 10 953 603,39

EU contribution

€ 9 494 326,25

Coordinated by

EXUS SOFTWARE MONOPROSOPI ETAIRIA PERIORISMENIS EVTHINIS
Greece

Periodic Reporting for period 2 - STAMINA (Demonstration of intelligent decision support for pandemic crisis prediction and management within and across European borders)

Reporting period: 2021-09-01 to 2023-02-28

Summary of the context and overall objectives of the project

STAMINA – “Demonstration of intelligent decision support for pandemic crisis prediction and management within and across European borders” is a two-year research and innovation project funded by the EU under the Horizon 2020 programme. The project, which started on September 2020, focuses on increasing the capability to manage pandemic situations, including diagnosis, prediction, and decision support and on improving preparedness and cooperation of the relevant stakeholders at national, regional and local level, within and across EU borders. The envisaged contributions of the project include: a) the application of Point of Care Testing (POCT) and smart wearable devices for first line screening and monitoring, b) tools for predictive modeling of pandemic outbreaks, evolution and impact, along with early warning functionalities and decision support for implementing mitigation strategies, c) a crisis simulation tool, defining the roles and responsibilities of key actors, and implementing different training scenarios, d) a Common Operational Picture (COP) platform, as the main interface for situation assessment and coordinated response of the involved actors, and e) real-time web and social media analytics for capturing societal feelings and reactions, raising awareness and increasing public trust in public health institutions and government authorities. The STAMINA toolset will be accompanied by a set of guidelines on effective implementation of risk communication principles and best practices in cross-organizational preparedness and response plans. The use of the STAMINA methods and tools will be demonstrated through 12 national and regional small-scale demonstrators and one large-scale cross-border simulation exercise involving all consortium partners

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

As the STAMINA project aims in providing recommendations and promoting best practices concerning pandemic management, a mapping of gaps in national legislation supporting policy measures was conducted. All gaps identified mainly fall under the pandemic management fields of operational preparedness, epidemiological surveillance and risk communication. The commonalities among the legal gaps mapped highlight opportunities for policy makers to enhance the pandemic management capacity and cross-border risk communication and data sharing.
With respect to final system design and architecture, the consortium described the system architecture, its various modes of operation and map user requirements to functional and non-functional requirements for individual tools and the platform as a whole.
Regarding the first tools’ release it was presented the status of the individual tools that will comprise the STAMINA Decision Support Toolset, for facilitating pandemic management. It has been shown that the tools cover the whole spectrum of supporting at strategic, tactical and operational decision level: predictive modeling, generation of early warnings and alerts, social media analytics, preparedness and training for the supervision of the pandemic evolution.
In regards to POCT analysis experiments by IPT, EV ILVO, ERASMUS MC have been performed regarding the performance of the novel sets of primers/probes, permitting the collection of information useful to optimize the necessary experimental assays for pathogen detection. Additionally, significant progress has been made in the validation of the POCT tools and the newly identified genetic biomarkers.
With respect to the exploitation steps towards the first period of the project, this has included: 1) the definition of the exploitation strategy 2) the identification of exploitation items 3) an initial market and competition assessment 4) the reporting on the individual exploitation plans. With respect to the trial process an initial description of the expected outcomes for the trial preparation the trial gaps and objectives, as well as the expected outcomes has been provided. Finally, in terms of the dissemination activities, the project has made significant progress in achieving most of the KPIs

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

In terms of major innovations impacts, seven predictive models have been developed based on four state-of-the-art modeling techniques ranging from Compartmental, Agent-based, Discrete-Event to Deep Neural Networks. Particularly. Regarding the EWS, the former extends on the service provisioning scheme of similar epidemic alerting and warning tools, such as the WHO Global Alert and Response platform, by introducing Artificial Intelligence and Deep Learning technologies that identify patterns from historical data and then perform pattern matching on new measurements to identify early signals of high-impact incidents. Examples of these is using historical patterns to predict the impact of an outbreak to the society, by calculating the predicted cases and deaths, based on previous outbreaks, and to the overall health care system, by assessing how the underlying infrastructure (e.g. ICU beds, relevant medical resources) will be impacted by such events. This can be crucial, as early predictions can allow authorities to plan on how to tackle these events, and reallocate resources. In order to produce these predictions a set of ML algorithms have been applied. Such as: Temporal Fusion Transformers and Long Short -Term Memory. Finally, by using ancillary sensing methods, such as the analysis of the viral content in water drains, the EWS can provide additional insights, increase the sensing accuracy and sensitivity and identify blind spots such as the presence of large numbers of asymptomatic cases.
Substantial findings and results have been additionally generated from WP6. Specifically, proprietary bioinformatics pipelines were employed in order to discover novel biomarkers, design new primer sets to cover all the pathogens under investigation.
Moreover, during the period of M01-M12, the identification of a novel strain of SARS-CoV-2 emerged.
While ethical impact is ongoing and expected to be more concretely engaged in the trials, at this stage we have been able to engage collaboratively with end-users and solution developers to identify potential ethical harms and benefits from the project. For instance, for the STAMINA models to provide insights at a granular enough level for the necessary decision-making, the data inputted into them needs to be demographically disaggregated. However, a consequence of that is assigning socio-cultural characteristics to predictions, potentially leading to stigmatization of segments of society.

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