Leveraging artificial intelligence for pandemic preparedness and response

Expected Outcome:

This topic aims at supporting activities that are enabling or contributing to one or several expected impacts of destination “Tackling diseases and reducing disease burden”. To that end, proposals under this topic should aim to deliver results that are directed, tailored towards and contributing to all the following expected outcomes:

• The potential of Artificial Intelligence (AI) is used in all aspects that determine optimal pandemic preparedness and response, and fast learning systems are supported, to the benefit of scientists, public health responders and policymakers. This includes using the full potential of available quality data for research and innovation to transform the development of medical, social or logistical countermeasures, as well as the detection, management and monitoring of emergencies at population levels, and the diagnosis, treatment, and prevention at the level of individuals.
• European pandemic preparedness and response benefits from readily available, trustworthy and ethical AI-based tools and technologies that enable it to act fast and in a targeted manner, to timely detect and understand emerging infectious threats, to respond adequately and proportionally to identified threats, and to control such threats effectively and efficiently.
• Different data types from multiple sources and disciplines across the EU and globally can be accessed, integrated and analysed by scientists, public health responders and policymakers, using trustworthy and ethical AI-based tools and technologies that support pandemic preparedness and response.

Scope:

The COVID-19 pandemic underscored the need of finding innovative approaches to pandemic preparedness and response, including digital solutions leveraging AI technologies. AI is a fast-developing field, holding an enormous potential in using the multitude of data from an equally vast range of sources, which should be used for improving preparedness and response to epidemics or pandemics in the EU and Associated Countries.

Examples from the COVID-19 pandemic response illustrate how advanced AI tools can enable efficient data use to support areas like forecasting, infectious disease surveillance and monitoring, development of medical interventions, timely diagnosis of infection, disease prognosis, or real-time monitoring of adherence to public health recommendations. New technologies with potentially high impact like air or wastewater real-time monitoring systems have also emerged.

These experiences and advances hold great potential for the future, but additional development and expansion of novel AI-based tools and technologies (including generative AI) is needed, while also further improving and testing existing ones. The use of AI on diverse datasets, as well as on their combinations within and across disciplines, can greatly increase the accuracy of assessments and predictions of medical (pharmaceutical or non-pharmaceutical) interventions in preparedness for, and response to epidemics and pandemics.

Research actions under this topic should include several of the following activities:

• Develop new, or improve existing AI-based tools, methods and technologies, geared towards greater safety, efficiency and impact of medical, societal or logistical countermeasures aiming at the prevention, containment or control of infectious disease epidemics or improved response management of health systems.
• Scout, assemble and prepare appropriate FAIR^[1] datasets generated across the EU and Associated Countries (e.g. COVID-19, Influenza, etc.), for the development, training and testing of targeted AI-supported generative assessment and prediction tools, in support of evidence-based policy and decision making for pandemic preparedness and response; in areas like surveillance and monitoring of infectious disease and disease dynamics, facilitating differential diagnosis, triage and risk group predictions, predicting drug response and disease progression, etc.
• Leverage the capacities of the existing and emerging data research infrastructures and the future European Health Data Space (EHDS)^[2] and the European Open Science Cloud (EOSC)^[3] architectures and research environments, while comprehensively addressing cybersecurity, data privacy, trustworthiness, equity and data quality, interoperability and access modalities.
• Identify and address the current technical, operational, and social limitations related to the (cross-border) access to quality data and to the smooth implementation of AI-driven solutions in the societal and legal context of the EU and Associated Countries.
• Engage with end-users, policymakers, regulatory bodies and authorities, and other stakeholders in the development, improvement, testing and validation of trustworthy and ethical AI-based tools and technologies, to propose options for the validation and uptake of the novel AI tools in real-world settings taking into consideration aspects like training needs, responsible use, users' trust, energy consumption, etc.

The participation of start-ups, micro, small and medium-sized enterprises (SMEs)^[4] is encouraged with the aim of strengthening their scientific and technological foundations, enhancing their innovation potential, and exploring possibilities for commercial exploitation.

Proposals selected for funding under this topic are expected to participate in joint activities as appropriate, which can take the form of project clustering, workshops, joint dissemination activities, etc. Applicants should anticipate budget to cover this collaboration.

This topic requires the effective contribution of social sciences and humanities (SSH) disciplines and the involvement of SSH experts, institutions as well as the inclusion of relevant SSH expertise, in order to produce meaningful and significant effects enhancing the societal impact of the related research activities.

Particular attention should be paid to detecting and mitigating gender, ethnicity and other biases, aiming to develop AI models that are fair, trustworthy, and beneficial for all. Proposals are encouraged to explore potential synergies with the projects funded under the topic HORIZON-CL4-2021-HUMAN-01-24: “Tackling gender, race and other biases in AI (RIA)”, as well as under the topic SC1-PHE-CORONAVIRUS-2020-2C: “Behavioural, social and economic impacts of the outbreak response”.

Applicants envisaging to include clinical studies^[5] should provide details of their clinical studies in the dedicated annex using the template provided in the submission system.

[1] See definition of FAIR data in the introduction to this work programme part.

[2] https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space_en

[3] https://research-and-innovation.ec.europa.eu/strategy/strategy-2020-2024/our-digital-future/open-science/european-open-science-cloud-eosc_en

[4] https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32003H0361

[5] Please note that the definition of clinical studies (see introduction to this work programme part) is broad and it is recommended that you review it thoroughly before submitting your application.