使用IP主机托管的数字接触追踪
Digital Contact Tracing Using IP Colocation
摘要：
传染病在人群中的传播可以用网络或图来建模。在数字广告中，互联网设备图是组织移动电话、PC、电视和平板电脑在互联网上访问媒体时产生的标识符的图形数据集。由于规模巨大，它们已经变得无处不在，因为它们可以进行有针对性的广告，内容定制和跟踪。本文假设互联网设备图，特别是那些基于IP托管，可以在预测和建模传染病的传播提供显着的效用。从2020年3月16日的那一周开始，在美国，许多人开始“就地避难”，因为全国各地的学校和工作场所因COVID-19大流行而关闭。本文通过研究一个拥有超过10亿节点的大规模互联网设备图，在命令生效之前和之后，量化了就地避难命令对该图的影响。效果是显而易见的。图表的结构表明，2020年4月12日至19日期间，美国发生了最不利于感染传播的行为。本文还讨论了设备图的效用，i）接触者追踪，ii）预测的“热点”，iii）模拟传染病传播，和iv）交付预防为基础的警告，以潜在的暴露个人。该论文还提出了一个首要问题：数字广告生态系统中的实体所积累的系统和数据集能否帮助抗击COVID-19？
英文摘要：
The spread of an infectious disease through a population can be modeled using a network or a graph. In digital advertising, internet device graphs are graph data sets that organize identifiers produced by mobile phones, PCs, TVs, and tablets as they access media on the internet. Characterized by immense scale, they have become ubiquitous as they enable targeted advertising, content customization and tracking. This paper posits that internet device graphs, in particular those based on IP colocation, can provide significant utility in predicting and modeling the spread of infectious disease. Starting the week of March 16th, 2020, in the United States, many individuals began to `shelter-in-place' as schools and workplaces across the nation closed because of the COVID-19 pandemic. This paper quantifies the effect of the shelter-in-place orders on a large scale internet device graph with more than a billion nodes by studying the graph before and after orders went into effect. The effects are clearly visible. The structure of the graph suggests behavior least conducive to transmission of infection occurred in the US between April 12th and 19th, 2020. This paper also discusses the utility of device graphs for i) contact tracing, ii) prediction of `hot spots', iii) simulation of infectious disease spread, and iv) delivery of advertisement-based warnings to potentially exposed individuals. The paper also posits an overarching question: can systems and datasets amassed by entities in the digital ad ecosystem aid in the fight against COVID-19?
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
