Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe
Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe
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DOI：
10.1038/s41586-020-2405-7
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发表时间：
2020-06-08
期刊：
影响因子：
64.8
通讯作者：
Bhatt, Samir
中科院分区：
文献类型：
--
作者：
Flaxman, Seth;Mishra, Swapnil;Bhatt, Samir
基于来自11个欧洲国家的汇总数据的建模表明，非药物干预-特别是封锁-对SARS-CoV-2的传播有显著影响，使感染的复制数低于1。在检测到新型冠状病毒（1）严重急性呼吸系统综合征冠状病毒2后，随着SARS-CoV-2及其在中国以外的传播，欧洲经历了2019年冠状病毒病（COVID-19）的大规模流行。对此，许多欧洲国家实施了非药物干预措施，如关闭学校和全国封锁。我们研究了自2020年2月COVID-19疫情爆发至2020年5月4日开始解除封锁期间，11个欧洲国家采取的重大干预措施的影响。我们的模型从观察到的死亡人数向后计算，以估计几周前发生的传播，考虑到感染和死亡之间的时滞。我们使用国家之间的部分信息池，对随时间变化的繁殖数（R-t）的个人和共享的影响。汇集允许使用更多的信息，有助于克服数据中的特质，并实现更及时的估计。我们的模型依赖于一些流行病学参数（如感染致死率）的固定估计，不包括输入或国家以下的变化，并假设R（t）的变化是对干预措施的直接反应，而不是行为的逐渐变化。在疫情持续的情况下，我们依赖不完整的死亡数据，这些数据在报告中显示出系统性偏差，并有待未来综合。我们估计，对于我们在此考虑的所有国家，目前的干预措施足以使R（t）低于1（R（t）< 1.0的概率大于99%），并实现对流行病的控制。我们估计，截至2020年5月4日，在所有11个国家中，共有1200万至1500万人感染了SARS-CoV-2，占人口的3.2%至4.0%。我们的研究结果表明，主要的非药物干预，特别是封锁，对减少传播有很大的影响。应考虑继续干预，以控制SARS-CoV-2的传播。
Modelling based on pooled data from 11 European countries indicates that non-pharmaceutical interventions-particularly lockdowns-have had a marked effect on SARS-CoV-2 transmission, driving the reproduction number of the infection below 1.Following the detection of the new coronavirus(1)severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics of coronavirus disease 2019 (COVID-19). In response, many European countries have implemented non-pharmaceutical interventions, such as the closure of schools and national lockdowns. Here we study the effect of major interventions across 11 European countries for the period from the start of the COVID-19 epidemics in February 2020 until 4 May 2020, when lockdowns started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks previously, allowing for the time lag between infection and death. We use partial pooling of information between countries, with both individual and shared effects on the time-varying reproduction number (R-t). Pooling allows for more information to be used, helps to overcome idiosyncrasies in the data and enables more-timely estimates. Our model relies on fixed estimates of some epidemiological parameters (such as the infection fatality rate), does not include importation or subnational variation and assumes that changes inR(t)are an immediate response to interventions rather than gradual changes in behaviour. Amidst the ongoing pandemic, we rely on death data that are incomplete, show systematic biases in reporting and are subject to future consolidation. We estimate that-for all of the countries we consider here-current interventions have been sufficient to driveR(t)below 1 (probabilityR(t) < 1.0 is greater than 99%) and achieve control of the epidemic. We estimate that across all 11 countries combined, between 12 and 15 million individuals were infected with SARS-CoV-2 up to 4 May 2020, representing between 3.2% and 4.0% of the population. Our results show that major non-pharmaceutical interventions-and lockdowns in particular-have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control.