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模拟暴露通知和非药物干预措施对华盛顿州新冠病毒传播的影响。

Modeling the effect of exposure notification and non-pharmaceutical interventions on COVID-19 transmission in Washington state.

作者信息

Abueg Matthew, Hinch Robert, Wu Neo, Liu Luyang, Probert William, Wu Austin, Eastham Paul, Shafi Yusef, Rosencrantz Matt, Dikovsky Michael, Cheng Zhao, Nurtay Anel, Abeler-Dörner Lucie, Bonsall David, McConnell Michael V, O'Banion Shawn, Fraser Christophe

机构信息

Google Research, Mountain View, CA, USA.

Nuffield Department of Medicine, University of Oxford, Oxford, UK.

出版信息

NPJ Digit Med. 2021 Mar 12;4(1):49. doi: 10.1038/s41746-021-00422-7.

Abstract

Contact tracing is increasingly used to combat COVID-19, and digital implementations are now being deployed, many based on Apple and Google's Exposure Notification System. These systems utilize non-traditional smartphone-based technology, presenting challenges in understanding possible outcomes. In this work, we create individual-based models of three Washington state counties to explore how digital exposure notifications combined with other non-pharmaceutical interventions influence COVID-19 disease spread under various adoption, compliance, and mobility scenarios. In a model with 15% participation, we found that exposure notification could reduce infections and deaths by approximately 8% and 6% and could effectively complement traditional contact tracing. We believe this can provide health authorities in Washington state and beyond with guidance on how exposure notification can complement traditional interventions to suppress the spread of COVID-19.

摘要

接触者追踪越来越多地被用于抗击新冠疫情,目前正在部署数字实施方案,其中许多基于苹果和谷歌的暴露通知系统。这些系统利用基于智能手机的非传统技术,在理解可能的结果方面存在挑战。在这项工作中,我们创建了华盛顿州三个县的个体模型,以探索数字暴露通知与其他非药物干预措施相结合,在不同的采用率、依从性和流动性情景下如何影响新冠病毒疾病传播。在一个参与率为15%的模型中,我们发现暴露通知可以将感染和死亡人数分别减少约8%和6%,并能有效补充传统的接触者追踪。我们相信,这可以为华盛顿州及其他地区的卫生当局提供指导,说明暴露通知如何补充传统干预措施以抑制新冠病毒的传播。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/011c/7955120/6bfb8a364695/41746_2021_422_Fig1_HTML.jpg

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