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Epidemic Modeling with Generative Agents

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arxiv 2307.04986 v1 pith:5YKOPRXC submitted 2023-07-11 cs.AI cs.MAecon.GNnlin.AOphysics.soc-phq-fin.EC

classification cs.AIcs.MAecon.GNnlin.AOphysics.soc-phq-fin.EC
keywords agentsepidemicgenerativemodelinghumanmodelwhenaddress
verification ladder T0 review T1 audit T2 compute T3 formal
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This study offers a new paradigm of individual-level modeling to address the grand challenge of incorporating human behavior in epidemic models. Using generative artificial intelligence in an agent-based epidemic model, each agent is empowered to make its own reasonings and decisions via connecting to a large language model such as ChatGPT. Through various simulation experiments, we present compelling evidence that generative agents mimic real-world behaviors such as quarantining when sick and self-isolation when cases rise. Collectively, the agents demonstrate patterns akin to multiple waves observed in recent pandemics followed by an endemic period. Moreover, the agents successfully flatten the epidemic curve. This study creates potential to improve dynamic system modeling by offering a way to represent human brain, reasoning, and decision making.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 16 citations worldwide. Full citation record

  1. How Affect Propagates among LLM Agents: Emergent Emotional Contagion in Crowd Simulation

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    Affect spreads among LLM crowd agents as spatial fronts, endemic plateaus, and personality-gated panic or anger solely via a perception–appraisal–expression loop, and the dynamics are backend-dependent.

  2. LLM-powered reasoning in agent-based modeling

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    HALE couples LLM group-level mobility decisions with large-scale activity-based ABM networks and better matches Salt Lake County COVID-19 peak timing and size than ABM-only runs.

  3. Negotiating Comfort: Simulating Personality-Driven LLM Agents in Shared Residential Social Networks

    cs.SI 2025-07 conditional novelty 6.0 of 10

    LLM-based generative agents simulate personality-driven temperature negotiations in a shared residential building, with positive personality traits associated with higher happiness and stronger friendships.

  4. Infected Smallville: How Disease Threat Shapes Sociality in LLM Agents

    physics.soc-ph 2025-06 reject novelty 5.0 of 10

    In three simulation runs, LLM agents primed with a swine flu article reduced social activity compared with controls, but the effect is confounded by prompt instructions and no inferential statistics are reported.

  5. Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications

    cs.MA 2025-07 conditional novelty 4.0 of 10

    The paper defines urban LLM agents, surveys their sensing, memory, reasoning, execution, and learning workflows, and organizes their applications across planning, transportation, environment, safety, and society.

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