用多源数据与网络模型实时发现疫情，提前两周预警变异株暴发。
Combining digital data streams and epidemic networks for real time outbreak detection
- 融合本地健康行为数据与区域间传播网络，构建可解释的检测框架。
- 在305个医疗区应用，可在病例数仅达峰值1/10时发现德尔塔、奥密克戎波峰。
- 揭示传统移动网络未捕捉到的疫情传播模式，适合公共卫生决策者使用。
及时应对疾病暴发需要密切监测其传播轨迹，但流行病时间序列中的高噪声阻碍了检测。跨数据源信息聚合在其他领域展现出显著降噪能力，但在流行病学中仍研究不足。本文提出LRTrend——一种可解释的机器学习框架，用于实时识别疫情暴发。该方法在单一区域内整合多样化的健康与行为数据流，并学习特定于疾病的流行病网络，实现跨区域信息聚合。我们揭示了美国境内多种未被常用人类流动网络解释的流行病聚集区与连接关系，可能为未来公共卫生协调提供重要参考。将LRTrend应用于2年新冠数据（覆盖305个医院转诊区域），可于疫情开始后2周内频繁检测到区域性的德尔塔与奥密克戎波峰，此时病例数仅为波峰总值的一小部分。
原文摘要 · Abstract (English)
Responding to disease outbreaks requires close surveillance of their trajectories, but outbreak detection is hindered by the high noise in epidemic time series. Aggregating information across data sources has shown great denoising ability in other fields, but remains underexplored in epidemiology. Here, we present LRTrend, an interpretable machine learning framework to identify outbreaks in real time. LRTrend effectively aggregates diverse health and behavioral data streams within one region and learns disease-specific epidemic networks to aggregate information across regions. We reveal diverse epidemic clusters and connections across the United States that are not well explained by commonly used human mobility networks and may be informative for future public health coordination. We apply LRTrend to 2 years of COVID-19 data in 305 hospital referral regions and frequently detect regional Delta and Omicron waves within 2 weeks of the outbreak's start, when case counts are a small fraction of the wave's resulting peak.
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