用智能体自动构建可验证的流行病模型，提升预测准确性与效率。
Agentic Framework for Epidemiological Modeling
- 将疾病传播建模为程序合成问题，通过流程图中间表示确保结构正确性。
- 在多种疫苗和免疫逃逸假设下，生成符合流行病学规律的反事实预测。
- 模拟专家工作流，自动纠错并加速收敛，适合公共卫生决策支持。
流行病建模对公共健康规划至关重要，但传统方法依赖固定模型类别，需人工重设计以应对病原体、政策和情景假设的变化。我们提出EPIAGENT，一个智能体框架，通过将疾病进展建模为迭代程序合成问题，实现流行病模拟器的自动合成、校准、验证与优化。核心设计是显式的流行病流动图中间表示，连接情景设定与模型结构，支持强而模块化的正确性检查，生成代码前即可验证。经验证的流程图被编译为机制性模型，支持在物理和流行病学约束下的可解释参数学习。在多个流行病情景案例研究中评估表明，EPIAGENT能捕捉复杂增长动态，并在不同疫苗接种与免疫逃逸假设下生成流行病学一致的反事实投影。结果表明，智能体反馈循环可防止模型退化，显著加速向有效模型的收敛，模拟专业专家工作流程。
原文摘要 · Abstract (English)
Epidemic modeling is essential for public health planning, yet traditional approaches rely on fixed model classes that require manual redesign as pathogens, policies, and scenario assumptions evolve. We introduce EPIAGENT, an agentic framework that automatically synthesizes, calibrates, verifies, and refines epidemiological simulators by modeling disease progression as an iterative program synthesis problem. A central design choice is an explicit epidemiological flow graph intermediate representation that links scenario specifications to model structure and enables strong, modular correctness checks before code is generated. Verified flow graphs are then compiled into mechanistic models supporting interpretable parameter learning under physical and epidemiological constraints. Evaluation on epidemiological scenario case studies demonstrates that EPIAGENT captures complex growth dynamics and produces epidemiologically consistent counterfactual projections across varying vaccination and immune escape assumptions. Our results show that the agentic feedback loop prevents degeneration and significantly accelerates convergence toward valid models by mimicking professional expert workflows.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。