To date, the only effective means to respond to the spreading of the COVID-19 pandemic are non-pharmaceutical interventions (NPIs), which entail policies to reduce social activity and mobility restrictions. Quantifying their effect is difficult, but it is key to reducing their social and economic consequences. Here, we introduce a meta-population model based on temporal networks, calibrated on the COVID-19 outbreak data in Italy and applied to evaluate the outcomes of these two types of NPIs. Our approach combines the advantages of granular spatial modelling of meta-population models with the ability to realistically describe social contacts via activity-driven networks. We focus on disentangling the impact of these two different types of NPIs: those aiming at reducing individuals’ social activity, for instance through lockdowns, and those that enforce mobility restrictions. We provide a valuable framework to assess the effectiveness of different NPIs, varying with respect to their timing and severity. Results suggest that the effects of mobility restrictions largely depend on the possibility of implementing timely NPIs in the early phases of the outbreak, whereas activity reduction policies should be prioritized afterwards.

迄今为止，应对COVID-19大流行蔓延的唯一有效方法是非药物干预（NPI），这需要采取减少社会活动和交通限制的政策。量化其影响是困难的，但这是减少其社会和经济后果的关键。在这里，我们介绍了一种基于时态网络的元人口模型，该模型根据意大利的COVID-19暴发数据进行了校准，并用于评估这两种类型的NPI的结果。我们的方法将元人口模型的精细空间建模的优势与通过活动驱动的网络现实地描述社交联系的能力结合在一起。我们专注于消除这两种不同类型的NPI的影响：旨在减少个人社会活动的NPI（例如通过锁定，以及那些实施行动不便的人。我们提供了一个有价值的框架来评估不同NPI的有效性，具体取决于时间和严重性。结果表明，出行限制的影响很大程度上取决于在爆发早期阶段及时实施非营利机构的可能性，而减少活动的政策应在事后优先考虑。