The impact of non-pharmaceutical interventions on SARS-CoV-2 transmission across 130 countries and territories.
The impact of non-pharmaceutical interventions on SARS-CoV-2 transmission across 130 countries and territories.
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非药物干预对SARS-CoV-2在130个国家和地区传播的影响。
DOI：
10.1186/s12916-020-01872-8
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发表时间：
2021-02-05
期刊：
影响因子：
9.3
通讯作者：
Jit M
中科院分区：
文献类型：
--
作者：
Liu Y;Morgenstern C;Kelly J;Lowe R;CMMID COVID-19 Working Group;Jit M
非药物干预（NPI）用于减少导致2019冠状病毒病（COVID-19）的SARS冠状病毒2（SARS-CoV-2）的传播。然而，具体非营利机构的有效性的经验证据并不一致。我们评估了130个国家和地区围绕内部遏制和关闭、国际旅行限制、经济措施和卫生系统行动对SARS-CoV-2传播的NPI的有效性。我们使用面板（纵向）回归来估计13类NPI在减少SARS-CoV-2传播方面的有效性，使用2020年1月至6月的数据。首先，我们使用层次聚类分析研究了NPI之间的时间关联。然后，我们根据不同的NPI回归了COVID-19随时间变化的繁殖数（Rt）。我们研究了不同的模型规格，以解释NPI和Rt变化之间的时间滞后，NPI强度水平，NPI效应随时间变化的变化，以及变量选择标准。结果进行了解释，同时考虑到模型规格的范围和时间聚类的NPI。有强有力的证据表明两个NPI之间存在关联（学校关闭，内部行动限制）和减少的Rt。（工作场所关闭，收入支持和债务/合同减免）在忽略其强度水平时具有强有力的有效性证据，而两个NPI（公共活动取消，限制集会）只有在评估其最大能力的执行情况时才有有力的证据表明其有效性（例如，限制1000人以上的聚会无效，限制10人以下的聚会有效）。关于其余非传染性疾病预防措施（居家要求、公共宣传运动、公共交通关闭、国际旅行控制、检测、接触者追踪）的有效性的证据不一致，也不确定。我们发现许多NPI之间的时间聚类。效应大小取决于我们是否包括峰值NPI强度后的数据。理解特定的NPI对SARS-CoV-2传播的影响是复杂的，因为时间聚集，效果的时间依赖性变化和NPI强度的差异。然而，关闭学校和内部行动限制的有效性在不同的模型规格中似乎很强，有一些证据表明，其他NPI在特定条件下也可能有效。这为政策制定者为应对COVID-19大流行而采取的许多（尽管不是全部）行动的潜在有效性提供了经验证据。在线版本包含补充材料，可通过10.1186/s12916-020-01872-8获取。
Non-pharmaceutical interventions (NPIs) are used to reduce transmission of SARS coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19). However, empirical evidence of the effectiveness of specific NPIs has been inconsistent. We assessed the effectiveness of NPIs around internal containment and closure, international travel restrictions, economic measures, and health system actions on SARS-CoV-2 transmission in 130 countries and territories. We used panel (longitudinal) regression to estimate the effectiveness of 13 categories of NPIs in reducing SARS-CoV-2 transmission using data from January to June 2020. First, we examined the temporal association between NPIs using hierarchical cluster analyses. We then regressed the time-varying reproduction number (Rt) of COVID-19 against different NPIs. We examined different model specifications to account for the temporal lag between NPIs and changes in Rt, levels of NPI intensity, time-varying changes in NPI effect, and variable selection criteria. Results were interpreted taking into account both the range of model specifications and temporal clustering of NPIs. There was strong evidence for an association between two NPIs (school closure, internal movement restrictions) and reduced Rt. Another three NPIs (workplace closure, income support, and debt/contract relief) had strong evidence of effectiveness when ignoring their level of intensity, while two NPIs (public events cancellation, restriction on gatherings) had strong evidence of their effectiveness only when evaluating their implementation at maximum capacity (e.g. restrictions on 1000+ people gathering were not effective, restrictions on < 10 people gathering were). Evidence about the effectiveness of the remaining NPIs (stay-at-home requirements, public information campaigns, public transport closure, international travel controls, testing, contact tracing) was inconsistent and inconclusive. We found temporal clustering between many of the NPIs. Effect sizes varied depending on whether or not we included data after peak NPI intensity. Understanding the impact that specific NPIs have had on SARS-CoV-2 transmission is complicated by temporal clustering, time-dependent variation in effects, and differences in NPI intensity. However, the effectiveness of school closure and internal movement restrictions appears robust across different model specifications, with some evidence that other NPIs may also be effective under particular conditions. This provides empirical evidence for the potential effectiveness of many, although not all, actions policy-makers are taking to respond to the COVID-19 pandemic. The online version contains supplementary material available at 10.1186/s12916-020-01872-8.
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影响因子：
29.9
作者：
Haug, Nils;Geyrhofer, Lukas;Klimek, Peter
通讯作者：
Klimek, Peter
DOI：
10.1073/pnas.2006520117
发表时间：
2020-07-21
影响因子：
11.1
作者：
Bertozzi, Andrea L.;Franco, Elisa;Sledge, Daniel
通讯作者：
Sledge, Daniel
影响因子：
56.9
作者：
Chinazzi, Matteo;Davis, Jessica T.;Vespignani, Alessandro
通讯作者：
Vespignani, Alessandro
影响因子：
6.1
作者：
HAUSMAN, JA
通讯作者：
HAUSMAN, JA
影响因子：
3.6
作者：
Alfano, Vincenzo;Ercolano, Salvatore
通讯作者：
Ercolano, Salvatore