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对住宿学院人群进行重复的 SARS-CoV-2 检测。

Repeat SARS-CoV-2 testing models for residential college populations.

机构信息

Department of Statistics and Data Science, Yale University, 24 Hillhouse Avenue, New Haven, CT, 06511-6814, USA.

Department of Biostatistics, Department of Ecology and Evolutionary Biology, Yale School of Management, Department of Statistics and Data Science, Yale School of Public Health, PO Box 208034, New Haven, CT, 06510, USA.

出版信息

Health Care Manag Sci. 2021 Jun;24(2):305-318. doi: 10.1007/s10729-020-09526-0. Epub 2020 Nov 17.

Abstract

Residential colleges are considering re-opening under uncertain futures regarding the COVID-19 pandemic. We consider repeat SARS-CoV-2 testing models for the purpose of containing outbreaks in the residential campus community. The goal of repeat testing is to detect and isolate new infections rapidly to block transmission that would otherwise occur both on and off campus. The models allow for specification of aspects including scheduled on-campus resident screening at a given frequency, test sensitivity that can depend on the time since infection, imported infections from off campus throughout the school term, and a lag from testing until student isolation due to laboratory turnaround and student relocation delay. For early- (late-) transmission of SARS-CoV-2 by age of infection, we find that weekly screening cannot reliably contain outbreaks with reproductive numbers above 1.4 (1.6) if more than one imported exposure per 10,000 students occurs daily. Screening every three days can contain outbreaks providing the reproductive number remains below 1.75 (2.3) if transmission happens earlier (later) with time from infection, but at the cost of increased false positive rates requiring more isolation quarters for students testing positive. Testing frequently while minimizing the delay from testing until isolation for those found positive are the most controllable levers for preventing large residential college outbreaks. A web app that implements model calculations is available to facilitate exploration and consideration of a variety of scenarios.

摘要

住宿学院正在考虑在不确定的 COVID-19 大流行未来的情况下重新开放。我们考虑了重复 SARS-CoV-2 检测模型,以控制住宿校园社区的疫情爆发。重复检测的目的是快速检测和隔离新的感染,以阻止校内和校外的传播。这些模型允许指定包括在特定频率下对在校居民进行定期筛查、检测灵敏度(灵敏度可以取决于感染后的时间)、整个学期来自校外的输入感染以及由于实验室周转和学生重新安置延迟而导致从检测到学生隔离的滞后等方面。对于 SARS-CoV-2 由感染年龄引起的早期(晚期)传播,如果每天每 10000 名学生有超过一次的输入暴露,那么每周筛查不能可靠地控制繁殖数大于 1.4(1.6)的疫情爆发。如果传播时间更早(更晚),每三天筛查一次可以控制疫情爆发,但代价是假阳性率增加,需要为检测呈阳性的学生提供更多隔离宿舍。在尽可能减少从检测到隔离阳性学生的延迟的同时,频繁检测是防止大型住宿学院疫情爆发的最可控手段。一个实现模型计算的网络应用程序可用于方便地探索和考虑各种场景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b74/7669307/885127da8419/10729_2020_9526_Fig1_HTML.jpg

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