arXiv:2606.06360v1 Announce Type: new Abstract: Modelling individual decision-making during infectious disease outbreaks is crucial for understanding behavioural dynamics and informing effective public health interventions. Prior work has shown that large language models can simulate realistic human behaviour by generating agent decisions based on demographic prompts and situational context. We build on this foundation with a spatially grounded, agent-based simulation framework that integrates LLM-generated decisions about self-reported influenza-like illness into a census-based synthetic popu
The increasing sophistication and generalisation capabilities of LLMs make them suitable for simulating complex human behaviours, intersecting with ongoing public health modelling needs.
This development allows for more realistic and granular modelling of infectious disease spread, offering better foresight for public health interventions and policy.
Traditional epidemiological models gain a new, more dynamic layer through LLM-driven agent decision-making, moving beyond static behavioural assumptions.
- · Public Health Agencies
- · Epidemiologists
- · AI/ML Researchers
- · Computational Social Scientists
- · Traditional static modelling approaches
Improved accuracy in predicting infectious disease trajectories and intervention effectiveness.
AI-driven policy recommendations could become a standard component of public health strategy development.
The application of LLMs to model complex societal dynamics extends beyond health, influencing urban planning and economic policy.
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