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ARXIV:2606.02867 · AGENTS · SUBMITTED 03 JUN · 20:49 UTC · FRESHNESS FRESH
ARXIV:2606.02867AGENTSSUBMITTED 03 JUN · 20:49 UTCFRESHNESS FRESHPetra Ferenz · Ava Keeling · Tobias O'Keefe · Lorenzo Stigliano · Francesco Di Lauro · Andres Colubri · +1 at arXiv
The Epi-LLM framework integrates agent-based models with LLMs to simulate human behavior during epidemics, offering a scalable environment for pandemic preparedness research.
Opportunity summary
Pain The Epi-LLM framework integrates agent-based models with LLMs to simulate human behavior during epidemics, offering a scalable environment for pandemic preparedness research.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
The Epi-LLM framework integrates agent-based models with LLMs to simulate human behavior during epidemics, offering a scalable environment for pandemic preparedness research. Here we introduce the Epi-LLM framework: a novel integration of agent-based modelling,…
Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging. Here we introduce the Epi-LLM framework: a novel integration of agent-based modelling, real-life epigames, and large language models (LLMs) in…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. This proof-of-principle work lays the groundwork for deploying the Epi-LLM framework as a scalable, risk-free simulation environment for pandemic preparedness research.
Agents moved forward this cycle; last verified June 2026. Public score 3.0/10.
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Score3.0Analysis summary
The Epi-LLM framework integrates agent-based models with LLMs to simulate human behavior during epidemics, offering a scalable environment for pandemic preparedness research.
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Paper Pack
10.48550/arXiv.2606.02867The Epi-LLM framework integrates agent-based models with LLMs to simulate human behavior during epidemics, offering a scalable environment for pandemic preparedness research.
Abstract
Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging. Here we introduce the Epi-LLM framework: a novel integration of agent-based modelling, real-life epigames, and large language models (LLMs) in which a synthetic society of agents reasons and adapts dynamically over an outbreak contact network. Comparing synthetic agent behaviour against a no-intervention SEIR baseline and human participant data from the AUIB epigame study, we find that LLM agents across four different architectures reduced peak active infections, with quarantine compliance peaking at 58-65% on day six of the 15-day simulation. A binomial generalised linear model showed that perceived health severity was the strongest predictor of quarantine behaviour ($β= 0.33, p = 0.002$), yielding a pseudo-$R^2$ of 0.055, comparable to the 0.072 observed in the human trial. LLM architecture is a key determinant of epidemic dynamics: low-variance architectures offer greater internal validity for testing behavioural rules, while high-variance models may better represent real-world decision-making. Geographic labels alone do not induce culturally differentiated behaviour; explicit attitudinal parameterisation is required. This proof-of-principle work lays the groundwork for deploying the Epi-LLM framework as a scalable, risk-free simulation environment for pandemic preparedness research.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 3.0
PROBLEM
The Epi-LLM framework integrates agent-based models with LLMs to simulate human behavior during epidemics, offering a scalable environment for pandemic preparedness research. Here we introduce the Epi-LLM framework: a novel integration of agent-based modelling, real-life epigame...
METHOD
Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging. Here we introduce the Epi-LLM framework: a novel integration of agent-based modelling, real-life epigames, and large language models (LLMs) in which a synthetic...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. This proof-of-principle work lays the groundwork for deploying the Epi-LLM framework as a scalable, risk-free simulation environment for pandemic preparedness research.
WHY NOW
Agents moved forward this cycle; last verified June 2026. Public score 3.0/10.
{"file name": "input.pdf", "number of pages": 14, "author": "Petra Ferenz; Ava Keeling; Tobias O'Keefe; Lorenzo Stigliano; Francesco Di Lauro; Andres Colubri; Jasmina Panovska-Griffiths"
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Concepts
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Materials
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The Epi-LLM framework integrates agent-based models with LLMs to simulate human behavior during epidemics, offering a scalable environment for pandemic preparedness research.
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Commercial read
3.0/10 public viability
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Build Passport
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reason
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proof status
unverified
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confidence low
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fresh
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Artifact maturity
GitHub and Hugging Face maturity payloads
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fresh
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Technical feasibility
partial
Current read
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Gaps
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Evidence
0 references, 3 sources, 50% evidence coverage.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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