论文标题
基于人类流动性数据量化COVID-19的传输风险：一种个性化的Pagerank方法，用于有效接触追踪
Quantifying COVID-19 transmission risks based on human mobility data: A personalized PageRank approach for efficient contact-tracing
论文作者
论文摘要
鉴于其广泛且持久的影响，Covid-19，尤其是其空间传播动力学，引起了很多关注。对这种动态的了解有助于公共卫生专业人员和城市经理设计和部署有效的接触追踪和治疗措施。但是，大多数现有的研究都集中在综合迁移率流动上，并且很少利用广泛可用的分类级人类流动性数据。在本文中，我们提出了一种个性化的Pagerank（PPR）方法，以根据人和位置的两分网络估算COVID-19传输风险。该方法均包含个体的迁移率模式及其时空相互作用。为了验证所提出的方法的适用性和相关性，我们研究了小型合成网络中COVID-19案例的传播与城市内迁移率模式之间的相互作用，以及基于公交智能卡数据的中国香港的真实世界移动网络。我们根据各种质量跟踪和测试策略（包括基于PPR的PPR，PAGERANK（PR）基于位置，基于路线，基于路线，基于路线和基本案例（无策略），比较了召回（灵敏度），准确性和Spearmans相关系数。结果表明，基于PPR的方法达到了最高的效率，准确性，并且Spearmans与实际情况数的相关系数。这证明了PPR对于传输风险估计的价值以及将单个移动性模式纳入有效接触和测试的重要性。
Given its wide-ranging and long-lasting impacts, COVID-19, especially its spatial spreading dynamics has received much attention. Knowledge of such dynamics helps public health professionals and city managers devise and deploy efficient contact-tracing and treatment measures. However, most existing studies focus on aggregate mobility flows and have rarely exploited the widely available disaggregate-level human mobility data. In this paper, we propose a Personalized PageRank (PPR) method to estimate COVID-19 transmission risks based on a bipartite network of people and locations. The method incorporates both mobility patterns of individuals and their spatiotemporal interactions. To validate the applicability and relevance of the proposed method, we examine the interplay between the spread of COVID-19 cases and intra-city mobility patterns in a small synthetic network and a real-world mobility network from Hong Kong, China based on transit smart card data. We compare the recall (sensitivity), accuracy, and Spearmans correlation coefficient between the estimated transmission risks and number of actual cases based on various mass tracing and testing strategies, including PPR-based, PageRank (PR)-based, location-based, route-based, and base case (no strategy). The results show that the PPR-based method achieves the highest efficiency, accuracy, and Spearmans correlation coefficient with the actual case number. This demonstrates the value of PPR for transmission risk estimation and the importance of incorporating individual mobility patterns for efficient contact-tracing and testing.
