用AI自动生成符合规范的疫情应急预案，大幅缩短制定时间。
EpiPlanAgent: Agentic Automated Epidemic Response Planning
- 基于大模型的多智能体系统，分解任务、结合知识库并模拟方案。
- 生成方案完整度和规范对齐度显著提升，开发时间大幅减少。
- 适合公共卫生部门快速响应突发疫情，提升应急准备效率。
疫情响应规划至关重要，但传统依赖人工，耗时费力。本研究设计并评估了EpiPlanAgent，一种基于智能体的系统，利用大语言模型（LLMs）自动化生成与验证数字应急响应计划。该多智能体框架整合了任务分解、知识锚定与仿真模块。在受控环境下，公共卫生专家使用真实疫情场景测试系统。结果表明，相比人工流程，EpiPlanAgent显著提升了计划的完整性与指南一致性，并大幅缩短开发时间。专家评估确认AI生成内容与人工撰写内容高度一致。用户反馈显示其具备强实用价值。结论：EpiPlanAgent为智能疫情响应规划提供了一种高效、可扩展的解决方案，展示了代理型AI在提升公共卫生准备能力方面的潜力。
原文摘要 · Abstract (English)
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.
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