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评估严重急性呼吸综合征冠状病毒2(SARS-CoV-2)在聚集活动中传播风险的简单数学建模方法。

Simple mathematical modelling approaches to assessing the transmission risk of SARS-CoV-2 at gatherings.

作者信息

Champredon David, Fazil Aamir, Ogden Nicholas H

机构信息

Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Guelph, ON.

Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St.-Hyacinthe, QC and Guelph, ON.

出版信息

Can Commun Dis Rep. 2021 May 7;47(4):184-194. doi: 10.14745/ccdr.v47i04a02.

Abstract

BACKGROUND

Gatherings may contribute significantly to the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). For this reason, public health interventions have sought to constrain unrepeated or recurrent gatherings to curb the coronavirus disease 2019 (COVID-19) pandemic. Unfortunately, the range of different types of gatherings hinders specific guidance from setting limiting parameters (e.g. total size, number of cohorts, the extent of physical distancing).

METHODS

We used a generic modelling framework, based on fundamental probability principles, to derive simple formulas to assess introduction and transmission risks associated with gatherings, as well as the potential efficiency of some testing strategies to mitigate these risks.

RESULTS

Introduction risk can be broadly assessed with the population prevalence and the size of the gathering, while transmission risk at a gathering is mainly driven by the gathering size. For recurrent gatherings, the cohort structure does not have a significant impact on transmission between cohorts. Testing strategies can mitigate risk, but frequency of testing and test performance are factors in finding a balance between detection and false positives.

CONCLUSION

The generality of the modelling framework used here helps to disentangle the various factors affecting transmission risk at gatherings and may be useful for public health decision-making.

摘要

背景

集会可能对严重急性呼吸综合征冠状病毒2(SARS-CoV-2)的传播起到重大推动作用。因此,公共卫生干预措施一直致力于限制单次或反复举行的集会,以遏制2019冠状病毒病(COVID-19)大流行。不幸的是,集会类型繁多,这阻碍了制定限制参数(如总规模、群组数量、物理距离程度)的具体指导。

方法

我们使用了一个基于基本概率原理的通用建模框架,推导出简单公式,以评估与集会相关的引入和传播风险,以及一些检测策略减轻这些风险的潜在效率。

结果

引入风险可通过人群患病率和集会规模大致评估,而集会中的传播风险主要由集会规模驱动。对于反复举行的集会,群组结构对群组间传播没有显著影响。检测策略可以降低风险,但检测频率和检测性能是在检测与假阳性之间找到平衡的因素。

结论

这里使用的建模框架具有通用性,有助于厘清影响集会传播风险的各种因素,可能对公共卫生决策有用。

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