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

被引：61

作者：

Abueg, Matthew ^{[1
]}

Hinch, Robert ^{[2
]}

Wu, Neo ^{[1
]}

Liu, Luyang ^{[1
]}

Probert, William ^{[2
]}

Wu, Austin ^{[1
]}

Eastham, Paul ^{[1
]}

Shafi, Yusef ^{[1
]}

Rosencrantz, Matt ^{[1
]}

Dikovsky, Michael ^{[1
]}

Cheng, Zhao ^{[1
]}

Nurtay, Anel ^{[2
]}

Abeler-Dorner, Lucie ^{[2
]}

Bonsall, David ^{[2
]}

McConnell, Michael V. ^{[1
,3
]}

O'Banion, Shawn ^{[1
]}

Fraser, Christophe ^{[2
]}

机构：

[1] Google Res, Mountain View, CA 94043 USA

[2] Univ Oxford, Nuffield Dept Med, Oxford, England

[3] Stanford Univ, Sch Med, Dept Med, Stanford, CA USA

来源：

NPJ DIGITAL MEDICINE | 2021年 / 4卷 / 01期

关键词：

HEALTH; DISEASE;

D O I：

10.1038/s41746-021-00422-7

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

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.

引用

页数：10

共 49 条

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