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[2510.00024] EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis
Summary
The paper presents EpidemIQs, a multi-agent framework utilizing large language models for efficient epidemic modeling, demonstrating improved performance in research automation.
Why It Matters
Epidemic modeling is crucial for public health responses. This framework leverages LLMs to streamline research processes, potentially enhancing the speed and accuracy of epidemic analyses, which is vital in managing health crises effectively.
Key Takeaways
- EpidemIQs integrates user inputs to automate literature review and analysis.
- The framework uses two types of agents for planning and task execution.
- It achieves a task success rate of 79% across various epidemic scenarios.
- EpidemIQs outperforms traditional single-agent LLM approaches.
- The cost of executing studies with EpidemIQs is approximately $1.57 per study.
Computer Science > Social and Information Networks arXiv:2510.00024 (cs) [Submitted on 24 Sep 2025 (v1), last revised 25 Feb 2026 (this version, v2)] Title:EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis Authors:Mohammad Hossein Samaei, Faryad Darabi Sahneh, Lee W. Cohnstaedt, Caterina Scoglio View a PDF of the paper titled EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis, by Mohammad Hossein Samaei and 3 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) offer new opportunities to accelerate complex interdisciplinary research domains. Epidemic modeling, characterized by its complexity and reliance on network science, dynamical systems, epidemiology, and stochastic simulations, represents a prime candidate for leveraging LLM-driven automation. We introduce EpidemIQs, a novel multi-agent LLM framework that integrates user inputs and autonomously conducts literature review, analytical derivation, network modeling, mechanistic modeling, stochastic simulations, data visualization and analysis, and finally documentation of findings in a structured manuscript, through five predefined research phases. We introduce two types of agents: a scientist agent for planning, coordination, reflection, and generation of final results, and a task-expert agent to focus exclusively on one specific duty serving as a tool to the scientist agent. The framework consistently generated complete reports in scientific artic...
