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“没有检测比错误的检测更好”吗?大规模检测中诊断不确定性对 COVID-19 传播的影响。

Is "no test is better than a bad test"? Impact of diagnostic uncertainty in mass testing on the spread of COVID-19.

机构信息

Institute for Risk and Uncertainty, University of Liverpool, Liverpool, United Kingdom.

Wirral & Liverpool University Teaching Hospitals, Birkenhead, United Kingdom.

出版信息

PLoS One. 2020 Oct 21;15(10):e0240775. doi: 10.1371/journal.pone.0240775. eCollection 2020.

DOI:10.1371/journal.pone.0240775
PMID:33085693
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7577497/
Abstract

Testing is viewed as a critical aspect of any strategy to tackle epidemics. Much of the dialogue around testing has concentrated on how countries can scale up capacity, but the uncertainty in testing has not received nearly as much attention beyond asking if a test is accurate enough to be used. Even for highly accurate tests, false positives and false negatives will accumulate as mass testing strategies are employed under pressure, and these misdiagnoses could have major implications on the ability of governments to suppress the virus. The present analysis uses a modified SIR model to understand the implication and magnitude of misdiagnosis in the context of ending lockdown measures. The results indicate that increased testing capacity alone will not provide a solution to lockdown measures. The progression of the epidemic and peak infections is shown to depend heavily on test characteristics, test targeting, and prevalence of the infection. Antibody based immunity passports are rejected as a solution to ending lockdown, as they can put the population at risk if poorly targeted. Similarly, mass screening for active viral infection may only be beneficial if it can be sufficiently well targeted, otherwise reliance on this approach for protection of the population can again put them at risk. A well targeted active viral test combined with a slow release rate is a viable strategy for continuous suppression of the virus.

摘要

测试被视为应对疫情的任何策略的关键方面。围绕测试的大部分讨论都集中在各国如何扩大能力上,但在询问测试是否足够准确以被使用之外,测试的不确定性几乎没有得到太多关注。即使是高度准确的测试,在大规模测试策略的压力下,也会出现假阳性和假阴性,这些误诊可能对政府抑制病毒的能力产生重大影响。本分析使用改进的 SIR 模型来理解在结束封锁措施的背景下误诊的影响和程度。结果表明,仅增加测试能力并不能为封锁措施提供解决方案。疫情的发展和感染高峰严重依赖于测试特征、测试目标和感染的流行程度。基于抗体的免疫护照被拒绝作为结束封锁的一种解决方案,因为如果目标定位不准确,它们可能会使民众面临风险。同样,如果能够充分针对性地进行主动病毒感染的大规模筛查,那么这种方法可能对保护民众有益,否则,依赖这种方法来保护民众可能会再次使他们面临风险。针对特定目标的主动病毒测试与缓慢释放率相结合,是持续抑制病毒的可行策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0352/7577497/185e8813b5c4/pone.0240775.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0352/7577497/185e8813b5c4/pone.0240775.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0352/7577497/185e8813b5c4/pone.0240775.g002.jpg

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