流行病建模的代理框架
Agentic Framework for Epidemiological Modeling
AI总结：
EPIAGENT通过代理框架自动合成和优化流行病模拟器，提升模型的准确性和效率。
摘要：
流行病建模对于公共卫生规划至关重要，但传统方法依赖于固定的模型类别，需要手动重新设计，因为病原体、政策和情景假设不断变化。我们引入了EPIAGENT，一种代理框架，通过将疾病发展建模为迭代程序合成问题，自动合成、校准、验证和细化流行病模拟器。一个关键设计选择是显式的流行病流图中间表示，它将情景规范链接到模型结构，并在生成代码之前启用强大的、模块化的正确性检查。经过验证的流图随后被编译成支持在物理和流行病学约束下可解释的参数学习的机理模型。在流行病情景案例研究中的评估表明，EPIAGENT能够捕捉复杂的增长动态，并在不同疫苗接种和免疫逃逸假设下产生流行病学一致的反事实预测。我们的结果表明，代理反馈循环可以防止退化，并通过模仿专业专家工作流程显著加速向有效模型的收敛。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
