Fajgenblat Maxime, Molenberghs Geert, Verbeeck Johan, Willem Lander, Crèvecoeur Jonas, Faes Christel, Hens Niel, Deboosere Patrick, Verbeke Geert, Neyens Thomas
Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute (DSI), UHasselt, Hasselt, Belgium.
Laboratory of Freshwater Ecology, Evolution and Conservation, KU Leuven, Leuven, Belgium.
Commun Med (Lond). 2024 Sep 11;4(1):178. doi: 10.1038/s43856-024-00600-0.
Across Europe, countries have responded to the COVID-19 pandemic with a combination of non-pharmaceutical interventions and vaccination. Evaluating the effectiveness of such interventions is of particular relevance to policy-makers.
We leverage almost three years of available data across 38 European countries to evaluate the effectiveness of governmental responses in controlling the pandemic. We developed a Bayesian hierarchical model that flexibly relates daily COVID-19 incidence to past levels of vaccination and non-pharmaceutical interventions as summarised in the Stringency Index. Specifically, we use a distributed lag approach to temporally weight past intervention values, a tensor-product smooth to capture non-linearities and interactions between both types of interventions, and a hierarchical approach to parsimoniously address heterogeneity across countries.
We identify a pronounced negative association between daily incidence and the strength of non-pharmaceutical interventions, along with substantial heterogeneity in effectiveness among European countries. Similarly, we observe a strong but more consistent negative association with vaccination levels. Our results show that non-linear interactions shape the effectiveness of interventions, with non-pharmaceutical interventions becoming less effective under high vaccination levels. Finally, our results indicate that the effects of interventions on daily incidence are most pronounced at a lag of 14 days after being in place.
Our Bayesian hierarchical modelling approach reveals clear negative and lagged effects of non-pharmaceutical interventions and vaccination on confirmed COVID-19 cases across European countries.
在欧洲，各国通过非药物干预措施和疫苗接种相结合的方式应对新冠疫情。评估此类干预措施的有效性对政策制定者尤为重要。
我们利用38个欧洲国家近三年的现有数据，评估政府应对措施在控制疫情方面的有效性。我们开发了一种贝叶斯分层模型，该模型将每日新冠发病率与过去的疫苗接种水平和非药物干预措施（如综合防疫指数所总结的）灵活关联起来。具体而言，我们采用分布滞后方法对过去的干预值进行时间加权，使用张量积平滑来捕捉两种干预措施之间的非线性和相互作用，并采用分层方法来简约地处理各国之间的异质性。
我们发现每日发病率与非药物干预措施的强度之间存在明显的负相关，同时欧洲国家之间的有效性存在很大差异。同样，我们观察到与疫苗接种水平存在强烈但更一致的负相关。我们的结果表明，非线性相互作用影响了干预措施的有效性，在高疫苗接种水平下，非药物干预措施的效果会减弱。最后，我们的结果表明，干预措施对每日发病率的影响在实施14天后最为明显。
我们的贝叶斯分层建模方法揭示了非药物干预措施和疫苗接种对欧洲国家确诊新冠病例具有明显的负面和滞后影响。
