AI Agents That Think Like People Could Predict How Epidemics Spread

For most of the past two decades, epidemiologists have grown remarkably good at modeling how a pathogen moves through a population. Contact networks, reproduction numbers, and mathematical projections of disease dynamics have become standard tools in the fight against outbreaks. Yet a stubborn problem has persisted at the heart of every one of those models: people do not behave like equations. During the COVID-19 pandemic, compliance with vaccination, mask-wearing, and mobility restrictions varied dramatically across social groups and over time, shaped by risk perception, trust in institutions, misinformation, socioeconomic environment, and material vulnerability. A consortium led by the University of Zaragoza, with participation from Universidad Carlos III de Madrid (UC3M) and Universitat Pompeu Fabra (UPF), now aims to close that gap with a new generation of artificial intelligence-driven epidemic models that place human behavior—not as a fixed parameter, but as a living, reasoning component—squarely at the center of disease forecasting.

The project, titled AI-Driven Behavioural Epidemic Models for Intervention Design (AIBEM), 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. AIBEM was one of just 32 projects chosen from 800 submitted proposals, a success rate of roughly 4 percent, and will receive €970,139 over three years. In total, the foundation will award €25.7 million to the projects selected in this edition. The consortium’s ambition is to build generative agent-based models, or GABMs, in which every simulated individual is powered by a large language model—the same underlying technology behind platforms such as ChatGPT or Gemini—configured with a specific personal profile, memory, goals, and tools.

“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,” said Yamir Moreno, the project’s principal investigator, Director of the Institute for Biocomputation and Physics of Complex Systems (BIFI) and a member of the Department of Theoretical Physics at the University of Zaragoza.

The technical core of AIBEM represents what the researchers describe as a paradigm shift. In conventional epidemiological modeling, human behavior is treated as exogenous—something imposed on the model from outside rather than emerging from it. Compliance rates are assumed, contact patterns are fixed, and the subtle feedback loops between policy, perception, and action are lost. In a generative agent-based model, by contrast, each simulated person can interact with others, make decisions, set expectations, and generate social norms. Trust in health authorities can erode or strengthen; misinformation can propagate through a simulated community; socioeconomic inequalities can shape who isolates, who keeps working, and who gets sick. The researchers say these agent responses will be systematically validated against experiments involving real human participants, a step they consider essential to keeping the simulations anchored in observed behavior rather than plausible-sounding fiction.

“UC3M’s main contribution is its extensive experience in designing and executing behavioral experiments with human participants,” said Anxo Sánchez, a 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.”

The work is divided into three main packages distributed across the consortium. UC3M will run behavioral experiments with socioeconomically diverse participants using the IBSEN platform, developed within the framework of the European H2020 project of the same name. The University of Zaragoza will build the generative agents, replicate human behavior, and integrate them into epidemiological models, drawing on BIFI’s supercomputing infrastructure—the CESAR system—and the accumulated 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 how the costs and benefits of each intervention are distributed across society. That distributional analysis is not an add-on, the team insists, but a defining feature of the project.

“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,” said 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 UPF.

In practical terms, AIBEM will treat equity as a primary model outcome rather than an afterthought. The age-based contact matrices that underpin most epidemic models will be expanded to include socioeconomic dimensions—occupation, income, and neighborhood—drawn from Spanish census data, labor force surveys, and stratified epidemiological records. This means disparities in disease burden and in the capacity to respond to an outbreak will emerge naturally from each simulated group’s material conditions, rather than being imposed by the modelers afterward. The approach echoes one of the hardest lessons of the COVID-19 pandemic: that a single national policy can produce profoundly unequal outcomes depending on who can work from home, who lives in crowded housing, and who can afford to isolate.

The project also embeds an ethical and governance framework aligned with the European AI Act, including bias audits, human oversight, and independent equity reviews. All models, datasets, and validation protocols will be published open-access following FAIR principles, making the methodology available for scrutiny and reuse. The expected deliverables include behavioral agents validated against real human experimental data with explicit socioeconomic diversity, coupled epidemiological models that quantify how behavioral heterogeneity modulates transmission, a systematic methodology for designing and evaluating interventions under realistic social conditions, and a decision-support toolkit for Spanish and European health authorities featuring policy briefs, ethical guidelines, and simulation software.

If the consortium succeeds, the implications could extend well beyond the next epidemic. Generative agent-based models are being explored across the social sciences as a way to simulate collective phenomena that resist traditional mathematical treatment, and AIBEM’s insistence on experimental validation against real participants offers a template for how such simulations might earn scientific credibility. For public health officials, the promise is more immediate: tools that can test, in silico, how a mask mandate, a vaccination campaign, or a lockdown will play out across communities with different levels of trust, income, and exposure—before those measures are imposed on real populations. The researchers caution that the challenge of capturing human nature remains formidable, but they argue that the arrival of generative AI has shifted the balance of that contest. The mathematics of pathogens, they note, has been solved; the mathematics of us is now within reach.

Subject of Research: Generative AI-driven behavioral epidemic modeling for public health intervention design

Article Title: UC3M participates in a pioneering scientific project using AI to anticipate human behavior during epidemics

Article References: UC3M participates in a pioneering scientific project using AI to anticipate human behavior during epidemics. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: AIBEM, epidemiological modeling, generative agents, large language models, agent-based models, human behavior, health inequalities, public health policy, UC3M, University of Zaragoza, behavioral experiments, la Caixa Foundation