感染风险评分:根据人类接触确定感染传播的风险

R. Agarwal, Abhik Banerjee
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引用次数: 8

摘要

在管理COVID-19等全球大流行事件的传播方面,已经采用了广泛的方法,并取得了不同程度的成功。鉴于这一流行病造成的大规模社会和经济影响以及持续时间的延长,重要的是不仅要控制这一疾病的传播,而且还要加倍努力采取措施,加快恢复社会和经济生活。因此,重要的是要确定具有高风险的情况,并在确定此类情况时尽早采取行动。虽然已经开发了大量的移动应用程序,但它们的目的是获取可用于追踪接触者的信息,而不是估计社交场合的风险。在本文中,我们引入了一种感染风险评分,它提供了对人类接触引起的感染风险的估计。使用真实世界的人类接触数据集,我们表明提出的风险评分可以提供对人群风险水平的现实估计。我们还描述了建议的感染风险评分如何在智能手机上实现。最后,我们确定了可以利用风险评分来最小化感染传播的代表性用例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Infection Risk Score: Identifying the risk of infection propagation based on human contact
A wide range of approaches have been applied to manage the spread of global pandemic events such as COVID-19, which have met with varying degrees of success. Given the large-scale social and economic impact coupled with the increasing time span of the pandemic, it is important to not only manage the spread of the disease but also put extra efforts on measures that expedite resumption of social and economic life. It is therefore important to identify situations that carry high risk, and act early whenever such situations are identified. While a large number of mobile applications have been developed, they are aimed at obtaining information that can be used for contact tracing, but not at estimating the risk of social situations. In this paper, we introduce an infection risk score that provides an estimate of the infection risk arising from human contacts. Using a real-world human contact dataset, we show that the proposed risk score can provide a realistic estimate of the level of risk in the population. We also describe how the proposed infection risk score can be implemented on smartphones. Finally, we identify representative use cases that can leverage the risk score to minimize infection propagation.
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