Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium.
Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium.
BMC Infect Dis. 2023 Jun 16;23(1):410. doi: 10.1186/s12879-023-08369-8.
Non-pharmaceutical interventions (NPIs) were adopted in Belgium in order to decrease social interactions between people and as such decrease viral transmission of SARS-CoV-2. With the aim to better evaluate the impact of NPIs on the evolution of the pandemic, an estimation of social contact patterns during the pandemic is needed when social contact patterns are not available yet in real time.
In this paper we use a model-based approach allowing for time varying effects to evaluate whether mobility and pre-pandemic social contact patterns can be used to predict the social contact patterns observed during the COVID-19 pandemic between November 11, 2020 and July 4, 2022.
We found that location-specific pre-pandemic social contact patterns are good indicators for estimating social contact patterns during the pandemic. However, the relationship between both changes with time. Considering a proxy for mobility, namely the change in the number of visitors to transit stations, in interaction with pre-pandemic contacts does not explain the time-varying nature of this relationship well.
In a situation where data from social contact surveys conducted during the pandemic are not yet available, the use of a linear combination of pre-pandemic social contact patterns could prove valuable. However, translating the NPIs at a given time into appropriate coefficients remains the main challenge of such an approach. In this respect, the assumption that the time variation of the coefficients can somehow be related to aggregated mobility data seems unacceptable during our study period for estimating the number of contacts at a given time.
为了减少人与人之间的社会互动，从而降低 SARS-CoV-2 的病毒传播，比利时采取了非药物干预（NPIs）措施。为了更好地评估 NPIs 对大流行演变的影响，当实时无法获得社会接触模式时，需要估计大流行期间的社会接触模式。
在本文中，我们使用了一种基于模型的方法，允许时变效应来评估流动性和大流行前的社会接触模式是否可用于预测 2020 年 11 月 11 日至 2022 年 7 月 4 日期间 COVID-19 大流行期间观察到的社会接触模式。
我们发现，特定地点的大流行前社会接触模式是估计大流行期间社会接触模式的良好指标。然而，这种关系随着时间的推移而变化。考虑到流动性的一个代理指标，即过境站访客数量的变化，与大流行前的接触相结合，并不能很好地解释这种关系的时变性质。
在大流行期间进行的社会接触调查数据尚未可用的情况下，使用大流行前社会接触模式的线性组合可能会证明是有价值的。然而，将特定时间的 NPIs 转化为适当的系数仍然是这种方法的主要挑战。在这方面，在我们的研究期间，假设系数的时间变化可以与聚合移动数据相关联，以便在给定时间估计接触次数，这似乎是不可接受的。
