Chair of Systems Design, ETH Zurich, Weinbergstrasse 56/58, 8092, Zurich, Switzerland.
Sci Data. 2024 Jul 13;11(1):774. doi: 10.1038/s41597-024-03534-3.
In the field of pharmaceutical supply chains, there is a lack of comprehensive historical data, representing a significant barrier to advancing research. To address this gap, we introduce a high-resolution dataset comprising drug packages distributed to approximately 300,000 pharmacies, hospitals, and practitioners across the US. We reconstruct 375 million distribution paths from ARCOS, a DEA-maintained database comprising half a billion shipping records between 2006 and 2014. While ARCOS tracks dyadic shipments, it does not provide information on the complete journey of single packages from manufacturers to final destinations. Our algorithm is able to reconstruct complete distribution paths from these dyadic records. The reconstructed dataset, with its high temporal and spatial resolution, offers an unprecedented view of US pharmaceutical distribution and is a valuable resource for investigating supply and distribution networks.
在药品供应链领域,缺乏全面的历史数据,这是推进研究的一个重大障碍。为了弥补这一空白,我们引入了一个高分辨率数据集,其中包含了在美国分发到大约 30 万家药店、医院和医生手中的药品包装。我们从 ARCOS 重建了 3.75 亿条分销路径,ARCOS 是一个由美国缉毒局维护的数据库,包含了 2006 年至 2014 年期间 5 亿条运输记录。虽然 ARCOS 可以跟踪二元运输,但它并没有提供关于单个包裹从制造商到最终目的地的完整旅程的信息。我们的算法能够从这些二元记录中重建完整的分销路径。这个具有高时间和空间分辨率的重建数据集提供了美国药品分销的前所未有的视角,是研究供应链和分销网络的宝贵资源。