Understanding the spread of SARS-CoV-2 has been one of the most pressing problems of the recent past. Network models present a potent approach to studying such spreading phenomena because of their ability to represent complex social interactions. While previous studies have shown that network centrality measures are generally able to identify influential spreaders in a susceptible population, it is not yet known if they can also be used to predict infection risks. However, information about infection risks at the individual level is vital for the design of targeted interventions. Here, we use large-scale administrative data from the Netherlands to study whether centrality measures can predict the risk and timing of infections with COVID-19-like diseases. We investigate this issue leveraging the framework of multi-layer networks, which accounts for interactions taking place in different contexts, such as workplaces, households and schools. In epidemic models simulated on real-world network data from over one million individuals, we find that existing centrality measures offer good predictions of relative infection risks, and are correlated with the timing of individual infections. We however find no association between centrality measures and real SARS-CoV-2 test data, which indicates that population-scale network data alone cannot aid predictions of virus transmission.

翻译：理解SARS-CoV-2的传播是近年来最紧迫的问题之一。网络模型因其能够表征复杂社会互动，成为研究此类传播现象的有效方法。尽管先前研究表明网络中心性度量通常能够识别易感人群中的关键传播者，但目前尚不清楚其是否也可用于预测感染风险。然而，个体层面的感染风险信息对于设计精准干预措施至关重要。本研究利用荷兰大规模行政数据，探究中心性度量能否预测COVID-19类疾病的感染风险及时间节点。我们借助多层网络框架（该框架整合工作场所、家庭和学校等不同场景下的社会互动）对此问题展开研究。在基于百万级真实人口数据的流行病模拟中，我们发现现有中心性度量能够有效预测相对感染风险，并与个体感染时间存在相关性。然而，中心性度量与实际SARS-CoV-2检测数据之间未发现关联，这表明仅凭群体规模网络数据无法辅助病毒传播预测。