论文标题
建模和预测社会距离和旅行限制对COVID-19的影响
Modelling and predicting the effect of social distancing and travel restrictions on COVID-19 spreading
论文作者
论文摘要
迄今为止，唯一有效响应Covid-19-19的大流行的方法是非药物干预措施（NPI），这需要政策减少社交活动和流动性限制。量化其效果很困难，但这是减少其社会和经济后果的关键。在这里，我们引入了基于时间网络的元群体模型，该模型在意大利的Covid-19爆发数据上进行了校准，并易于评估这两种NPI的结果。我们的方法结合了元群模型的颗粒空间建模的优势，并能够通过活动驱动的网络实际描述社会接触的能力。我们提供了一个有价值的框架来评估不同NPI的生存能力，这些框架在其时间和严重性方面有所不同。结果表明，移动性限制的影响很大程度上取决于在暴发的早期阶段实施及时的NPI的可能性，而随后应优先考虑降低活动政策。
To date, the only effective means to respond to the spreading of 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 reduce their social and economical consequences. Here, we introduce a meta-population model based on temporal networks, calibrated on the COVID-19 outbreak data in Italy and apt 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 provide a valuable framework to assess the viability of different NPIs, varying with respect to their timing and severity. Results suggest that the effects of mobility restrictions largely depend on the possibility to implement timely NPIs in the early phases of the outbreak, whereas activity reduction policies should be prioritised afterwards.
