Advancing Pandemic Preparedness and Response through High-Quality Data and AI
A four-year European research and innovation action that builds trustworthy, explainable AI tools to help
Europe anticipate, detect and respond to future pandemics, including unknown pathogens (“Disease X”).
PPKE ITK leads the project's population and outbreak modelling work package.
Recent pandemic experience exposed fragmented health data, limited forecasting ability, weak early-warning
systems, and difficult decision-making under uncertainty. AURORAI addresses these barriers by developing a
unified platform of explainable, ethical, and fast-learning AI tools built on high-quality, trustworthy data.
Social sciences and humanities expertise is embedded throughout the project to ensure societal relevance,
ethical compliance, and fairness. The tools will be validated across four use cases and at EU, national, and
local levels, giving decision-makers actionable intelligence to reduce the burden of future pandemics and
strengthen Europe's health resilience.
AURORAI is a Horizon Europe Research and Innovation Action under the Health cluster call
HORIZON-HLTH-2025-01 (topic DISEASE-04, Leveraging artificial intelligence for pandemic preparedness and
response), managed by the European Health and Digital Executive Agency (HaDEA).
At a Glance
Acronym: AURORAI
Grant agreement: 101290677
Programme: Horizon Europe (Health)
Type of action: Research and Innovation Action
Runtime: 1 Sep 2026 – 31 Aug 2030
Coordinator: IDENER Research & Development AIE (Spain)
PPKE: beneficiary, leader of WP4
Goals of AURORAI
The main objective of AURORAI is to strengthen pandemic preparedness and response so that the EU can act swiftly
and effectively against future health threats, including unknown pathogens. To do so, the project delivers five
connected innovations that converge in a single AURORAI platform.
1
Decentralised Knowledge Graph (DKG)
A trusted, FAIR-compliant data backbone that enables secure, interoperable, and bias-aware cross-border
use of health data while preserving data sovereignty.
2
Multi-Pathogen Disease X Modelling Tool (MP-DX)
Rapidly adaptable, explainable models of disease dynamics, grounded in a One Health approach and enhanced
by transfer learning for emerging threats.
3
Early Warning and Outbreaks Prediction Tool (EW-OP) PPKE-led WP
Combines demographic, behavioural, and mobility data to deliver timely forecasts, “what-if”
scenarios, and evaluation of pharmaceutical and non-pharmaceutical countermeasures.
4
AI-agent based Decision Support System (DSS)
Integrates heterogeneous data, expert knowledge, and causal reasoning into multi-criteria analyses that
support proportionate, evidence-based interventions.
5
Knowledge Hub (KH)
A space for co-creation with researchers, healthcare professionals, public health authorities,
policymakers, and citizens, enhancing transparency, trust, and uptake.
PPKE's Role in AURORAI
Pázmány Péter Catholic University, Faculty of Information Technology and Bionics (PPKE ITK) leads
Work Package 4 – Behaviour-Aware, AI-Enhanced Population Modelling for Pandemic Preparedness.
WP4 develops a modular, AI-enhanced, and privacy-preserving EW-OP tool for timely, data-driven public health
scenario modelling at national and cross-border scales. The institutional PI at PPKE is
Prof. István Reguly.
Contributions Beyond WP4
Data infrastructure (WP2)
Defining the population, mobility and behavioural data our models need, and working with the synthetic data generation pipeline where real data are scarce.
Demonstration (WP6)
Co-design with end users and demonstrations of EW-OP scenarios as part of the project's use cases.
Ethics & society (WP1)
Ethical and legal requirements for synthetic populations, vulnerability attributes and mobility data used in outbreak modelling.
Decision support (WP5)
Feeding EW-OP outputs, such as counterfactual intervention scenarios, into the AI-agent based decision support system.
PanSim: The Engine We Bring
PanSim is our open-source, high-resolution agent-based epidemic microsimulator. Agents
follow daily schedules across locations, and transmission is modelled stochastically at the locations they
share. It supports interventions such as testing, quarantine, closures and vaccination prioritisation, and runs
on multi-core CPUs (OpenMP) and GPUs (CUDA).
Developed from 2020 onwards for COVID-19 modelling in Hungary, PanSim was originally applied at city scale
(Szeged) and combined with control-theoretic methods to design interventions. In AURORAI we are scaling it
to national level and connecting it with synthetic populations, mobility data, federated simulation and AI
surrogates.
AURORAI has received funding from the European Union's Horizon Europe research and innovation programme
under grant agreement No. 101290677.
Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of
the European Union or the European Health and Digital Executive Agency (HaDEA). Neither the European Union
nor the granting authority can be held responsible for them.
