A consortium led by the University de Zaragoza, with the participation of Universidad Carlos III de Madrid (UC3M), has been selected in the 2026 Health Research Call by the ”la Caixa” Foundation—one of the most competitive biomedical research funding calls in Spain and Portugal. The AIBEM project will develop artificial intelligence (AI)-driven epidemiological models capable of replicating how trust in health authorities, adherence to guidelines, misinformation, socioeconomic inequalities, and social determinants influence the spread of disease, while simultaneously evaluating the effectiveness of public health measures to mitigate community transmission.

“For two decades, science has allowed us to accurately model the dynamics of pathogen spread through contact networks. However, the true challenge has always been the unpredictability of our own nature. Capturing the complexity of human behavior during a health crisis remains an unresolved challenge. The rise of generative AI changes the rules of the game in our favor: by equipping autonomous computational agents with reasoning capabilities and mutual influence, we can transform epidemiological models from mere mathematical projections into a faithful reflection of the human factor on a societal scale,” states the project's principal investigator, Yamir Moreno, Director of the Institute for Biocomputation and Physics of Complex Systems (BIFI) and member of the Department of Theoretical Physics at the University de Zaragoza.

“UC3M's main contribution is its extensive experience in designing and executing behavioral experiments with human participants,” notes Anxo Sánchez, another researcher on the project from UC3M's Interdisciplinary Group of Complex Systems (GISC). “This capability is key, as validating generative agents requires not only comparing their behavior against standard experiments, but also modeling epidemic-related scenarios and designing experiments that faithfully replicate them—many of which have not been addressed before and will be crucial to the project's success,” he concludes.

Models aimed at emulating human behavior

The COVID-19 pandemic demonstrated that compliance with measures such as vaccination, mask-wearing, or mobility restrictions varied significantly across social groups and over time, driven by risk perception, trust in institutions, misinformation, socioeconomic environment, vulnerability, and material inequalities. Nevertheless, standard epidemiological models continue to treat human behavior as a fixed or exogenous parameter, significantly limiting their utility as decision-making tools.

In this regard, AIBEM proposes a paradigm shift: generative agent-based models (GABMs), where each simulated individual is powered by a large language model (LLM) configured with a specific profile. This is the same underlying technology powering AI platforms like ChatGPT or Gemini. These agents will possess their own identity, goals, memory, and tools. Within this framework, they will interact with one another, make decisions, set expectations, and generate social norms. Furthermore, their responses will be systematically validated against experiments involving real human participants.

“Epidemics are not purely biological phenomena: their spread and consequences are deeply conditioned by social inequalities, living and working conditions, and power dynamics. Therefore, developing technologically more sophisticated epidemiological models is not enough if they fail to incorporate the structural drivers that leave certain social groups more exposed, more vulnerable, or less equipped to protect themselves. At UPF, we will contribute our expertise in ecosocial determinants, health inequalities, and public policy, leading the design and evaluation of interventions that consider both efficacy and equity impacts. Our challenge is to ensure that AI goes beyond better predicting epidemics to helping explain them and developing socially just public health responses—built with stakeholder participation and capable of reducing, rather than reproducing, existing inequalities,” notes another researcher on the project, Joan Benach, Director of the Health Inequalities Research Group – Employment Conditions Network (GREDS-EMCONET) and Co-Director of the JHU-UPF Public Policy Center at Universitat Pompeu Fabra (UPF).

The project is structured into three main work packages. UC3M will conduct behavioral experiments with socioeconomically diverse participants using the IBSEN platform, developed within the framework of the European H2020 project of the same name. Meanwhile, the University of Zaragoza will develop generative agents that replicate human behavior and integrate them into epidemiological models, leveraging BIFI's supercomputing infrastructure (CESAR) and the extensive expertise of the Complex Networks and Systems Group (COSNET) in mathematical and computational epidemic modeling. UPF will translate the results into decision-support tools for health authorities, including an explicit assessment of the distributional impact of each intervention.

Equity as a model outcome rather than an afterthought

One of AIBEM's defining features is that equity is not appended at the end of the analysis, but treated as a primary outcome of the model itself. Age-based contact matrices will be expanded to include socioeconomic dimensions—occupation, income, neighborhood—drawn from Spanish census data, labor force surveys, and stratified epidemiological records, ensuring that disparities in disease burden and response capacity emerge naturally from each group's material conditions.

The project also incorporates an ethical and governance framework aligned with the European AI Act (EU AI Act), featuring bias audits, human oversight, and independent equity reviews. All models, datasets, and validation protocols will be published open-access following FAIR principles.

Expected deliverables include behavioral agents validated against real human experimental data with explicit socioeconomic diversity, coupled epidemiological models quantifying how behavioral heterogeneity modulates transmission, a systematic methodology to design and evaluate interventions under realistic social conditions, and a decision-support toolkit for Spanish and European health authorities featuring policy briefs, ethical guidelines, and simulation software.

The project, titled AI-Driven Behavioural Epidemic Models for Intervention Design (AIBEM), will receive €970,139 over three years. AIBEM was one of 32 projects selected out of 800 proposals submitted to the 2026 call, representing a success rate of 4%. In total, the ”la Caixa” Foundation will award €25.7 million to the projects selected in this edition.