中文标题#
超越傳統監控：利用專家知識進行公共衛生預測
英文标题#
Beyond Traditional Surveillance: Harnessing Expert Knowledge for Public Health Forecasting
中文摘要#
2025 年美國公共衛生人員的縮減會加劇公共衛生危機中的潛在風險。 來自公共衛生官員的專家判斷是一種重要的信息來源，不同於傳統的監測基礎設施，應予以重視 —— 而非拋棄。 了解在限制條件下專家知識如何運作，對於理解能力減少的潛在影響至關重要。 為了探討專家預測能力，在 2024 年 CSTE 研討會上的 114 名公共衛生官員生成了 103 個關於峰值住院人數的預測以及 102 個理由，以及 114 個關於 2024/25 賽季賓夕法尼亞州流感 H3 與 H1 占主導地位的預測。 我們將專家預測與計算模型進行了比較，並使用理由通過潛在狄利克雷分佈分析推理模式。 專家更好地預測了 H3 的主導地位，並對不合理的場景賦予較低的概率，而不是模型。 專家的理由借鑒了歷史模式、病原體相互作用、疫苗數據和累積經驗。 專家的公共衛生知識構成了一種關鍵的數據來源，應與傳統數據集同等重視。 我們建議開發一個國家工具包，系統地收集和分析專家預測和理由，將人類判斷視為可量化的數據，與監測系統一起，以增強危機應對能力。
英文摘要#
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.
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