用真实人流数据构建图模型，自动追踪无症状传播路径。
Graph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data
- 提出传染路径中心性度量，结合图学习识别关键传播事件。
- 仅30%感染者检测时，双向追踪使有效再生数下降71%。
- 适合疫情早期防控与无症状传播研究者参考。
对于传播迅速且多数病例无症状的疾病，快速有效的接触追踪至关重要。尽管暴露通知应用可发出潜在暴露警告，但仍需全自动系统追踪感染传播路径。本研究利用大规模真实人类移动数据构建接触网络，提出新的传染路径中心性（Infectious Path Centrality）指标，结合图学习边分类器识别关键传播事件，取得94%的F1分数。同时探索双向接触追踪——对已感染者进行回溯隔离，并对潜在暴露者提前干预——并与仅在确诊后隔离的传统前向追踪对比。结果表明，当仅30%的有症状者被检测时，双向追踪可使有效再生数降低71%，显著控制疫情扩散。
原文摘要 · Abstract (English)
For rapidly spreading diseases where many cases show no symptoms, swift and effective contact tracing is essential. While exposure notification applications provide alerts on potential exposures, a fully automated system is needed to track the infectious transmission routes. To this end, our research leverages large-scale contact networks from real human mobility data to identify the path of transmission. More precisely, we introduce a new Infectious Path Centrality network metric that informs a graph learning edge classifier to identify important transmission events, achieving an F1-score of 94%. Additionally, we explore bidirectional contact tracing, which quarantines individuals both retroactively and proactively, and compare its effectiveness against traditional forward tracing, which only isolates individuals after testing positive. Our results indicate that when only 30% of symptomatic individuals are tested, bidirectional tracing can reduce infectious effective reproduction rate by 71%, thus significantly controlling the outbreak.
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