用移动数据建模合成阿片类药物扩散，揭示其空间传播规律
Mobility-GCN: a human mobility-based graph convolutional network for tracking and analyzing the spatial dynamics of the synthetic opioid crisis in the USA, 2013-2020
- 基于人类流动构建图卷积网络，融合时空数据
- 发现2013-2020年合成阿片致死率持续上升，远超海洛因
- 适合公共卫生与流行病学研究者参考
合成阿片是美国药物相关过量死亡中最常见的药物。据美国疾病控制与预防中心报告，2018年约70%的药物过量死亡涉及阿片类药物，其中67%的阿片类死亡由合成阿片导致。本研究分析了2013至2020年间美国合成阿片类药物致死事件的时空演变，探讨其与海洛因致死模式的空间关联，并比较两种药物致死趋势。通过将县际空间连接与人类流动信息融入图卷积神经网络（Mobility-GCN），建模并分析了合成阿片类药物在海洛因流行背景下的传播动态。
原文摘要 · Abstract (English)
Synthetic opioids are the most common drugs involved in drug-involved overdose mortalities in the U.S. The Center for Disease Control and Prevention reported that in 2018, about 70% of all drug overdose deaths involved opioids and 67% of all opioid-involved deaths were accounted for by synthetic opioids. In this study, we investigated the spread of synthetic opioids between 2013 and 2020 in the U.S. We analyzed the relationship between the spatiotemporal pattern of synthetic opioid-involved deaths and another key opioid, heroin, and compared patterns of deaths involving these two types of drugs during this period. Spatial connections and human mobility between counties were incorporated into a graph convolutional neural network model to represent and analyze the spread of synthetic opioid-involved deaths in the context of previous heroin-involved death patterns.
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