标题： 超越传统监控：利用专家知识进行公共卫生预测 显示英文标题

标题： Beyond Traditional Surveillance: Harnessing Expert Knowledge for Public Health Forecasting

摘要： 2025年美国公共卫生人员的缩减会加剧公共卫生危机中的潜在风险。 来自公共卫生官员的专家判断是一种重要的信息来源，不同于传统的监测基础设施，应予以重视——而非抛弃。 了解在限制条件下专家知识如何运作，对于理解能力减少的潜在影响至关重要。 为了探讨专家预测能力，在2024年CSTE研讨会上的114名公共卫生官员生成了 103个关于峰值住院人数的预测以及102个理由，以及114个关于2024/25赛季宾夕法尼亚州流感H3与H1占主导地位的预测。 我们将专家预测与计算模型进行了比较，并使用理由通过潜在狄利克雷分布分析推理模式。 专家更好地预测了H3的主导地位，并对不合理的场景赋予较低的概率，而不是模型。 专家的理由借鉴了历史模式、病原体相互作用、疫苗数据和累积经验。 专家的公共卫生知识构成了一种关键的数据来源，应与传统数据集同等重视。 我们建议开发一个国家工具包，系统地收集和分析专家预测和理由，将人类判断视为可量化的数据，与监测系统一起，以增强危机应对能力。 显示英文摘要

摘要： Downsizing the US public health workforce throughout 2025 amplifies potential risks during public health crises. Expert judgment from public health officials represents a vital information source, distinct from traditional surveillance infrastructure, that should be valued -- not discarded. Understanding how expert knowledge functions under constraints is essential for understanding the potential impact of reduced capacity. To explore expert forecasting capabilities, 114 public health officials at the 2024 CSTE workshop generated 103 predictions plus 102 rationales of peak hospitalizations and 114 predictions of influenza H3 versus H1 dominance in Pennsylvania for the 2024/25 season. We compared expert predictions to computational models and used rationales to analyze reasoning patterns using Latent Dirichlet Allocation. Experts better predicted H3 dominance and assigned lower probability to implausible scenarios than models. Expert rationales drew on historical patterns, pathogen interactions, vaccine data, and cumulative experience. Expert public health knowledge constitutes a critical data source that should be valued equally with traditional datasets. We recommend developing a national toolkit to systematically collect and analyze expert predictions and rationales, treating human judgment as quantifiable data alongside surveillance systems to enhance crisis response capabilities.