EpiPlanAgent：基于智能体的自动化流行病应对规划系统 (EpiPlanAgent: Agentic Automated Epidemic Response Planning)

Epidemic response planning is essential yet traditionally reliant on labor-intensive manual methods. This study aimed to design and evaluate EpiPlanAgent, an agent-based system using large language models (LLMs) to automate the generation and validation of digital emergency response plans. The multi-agent framework integrated task decomposition, knowledge grounding, and simulation modules. Public health professionals tested the system using real-world outbreak scenarios in a controlled evaluation. Results demonstrated that EpiPlanAgent significantly improved the completeness and guideline alignment of plans while drastically reducing development time compared to manual workflows. Expert evaluation confirmed high consistency between AI-generated and human-authored content. User feedback indicated strong perceived utility. In conclusion, EpiPlanAgent provides an effective, scalable solution for intelligent epidemic response planning, demonstrating the potential of agentic AI to transform public health preparedness.

翻译：流行病应对规划至关重要，但传统上依赖于劳动密集型的非自动化方法。本研究旨在设计并评估EpiPlanAgent——一个基于智能体、利用大语言模型（LLMs）自动生成和验证数字化应急响应计划的系统。该多智能体框架整合了任务分解、知识嵌入和模拟模块。公共卫生专业人员通过受控评估，使用真实世界疫情场景对系统进行了测试。结果表明，与人工流程相比，EpiPlanAgent显著提升了计划的完整性和指南符合度，同时大幅缩短了制定时间。专家评估确认了AI生成内容与人工撰写内容具有高度一致性。用户反馈显示出强烈的感知实用性。综上所述，EpiPlanAgent为智能流行病应对规划提供了一种高效、可扩展的解决方案，展现了智能体AI变革公共卫生应急准备能力的潜力。