Since the WHO declared the COVID-19 pandemic on March 11, 2020, the novel coronavirus, SARS-CoV-2, has profoundly impacted public health and the economy worldwide. But there are not the only ones to be hit. The COVID-19 pandemic has also substantially altered mental health, with anxiety symptoms being one of the most frequently reported problems. Especially, the number of people reporting anxiety symptoms increased significantly during the first lockdown-phase compared to similar data collected before the pandemic. Yet, most of these studies relied on a unitary approach to anxiety, wherein its different constitutive features (i.e., symptoms) were tallied into one sum-score, thus ignoring any possibility of interactions between them. Therefore, in this study, we seek to map the associations between the core features of anxiety during the first weeks of the first Belgian COVID-19 lockdown-phase (n = 2,829). To do so, we implemented, in a preregistered fashion, two distinct computational network approaches: a Gaussian graphical model and a Bayesian network modelling approach to estimate a directed acyclic graph. Despite their varying assumptions, constraints, and computational methods to determine nodes (i.e., the variables) and edges (i.e., the relations between them), both approaches pointed to excessive worrying as a node playing an especially influential role in the network system of the anxiety features. Altogether, our findings offer novel data-driven clues for the ongoing field's larger quest to examine, and eventually alleviate, the mental health consequences of the COVID-19 pandemic.

中文翻译：

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过度担忧是比利时第一个 COVID-19 封锁阶段焦虑的核心特征：来自网络方法的见解。

自世界卫生组织于 2020 年 3 月 11 日宣布 COVID-19 大流行以来，新型冠状病毒 SARS-CoV-2 对全球公共卫生和经济产生了深远影响。但受到打击的并非只有这些。COVID-19 大流行还极大地改变了心理健康，焦虑症状是最常报告的问题之一。特别是，与大流行前收集的类似数据相比，在第一个封锁阶段报告焦虑症状的人数显着增加。然而，大多数这些研究依赖于一种统一的焦虑方法，其中不同的构成特征（即症状）被计入一个总分，从而忽略了它们之间相互作用的任何可能性。因此，在本研究中，我们试图绘制比利时 COVID-19 第一个封锁阶段 (n = 2,829) 的头几周内焦虑核心特征之间的关联。为此，我们以预先注册的方式实现了两种不同的计算网络方法：高斯图模型和贝叶斯网络建模方法来估计有向无环图。尽管它们的假设、约束和确定节点（即变量）和边（即它们之间的关系）的计算方法各不相同，但这两种方法都指出了过度担忧，因为节点在网络系统中扮演着特别有影响力的角色。焦虑特征。总而言之，我们的研究结果为该领域正在进行的更大范围的探索提供了新颖的数据驱动线索，以检查并最终减轻 COVID-19 大流行对心理健康的影响。