Despite a number of successful approaches in predicting the spatiotemporal patterns of the novel coronavirus (COVID-19) pandemic and quantifying the effectiveness of non-pharmaceutical interventions starting from data about the initial outbreak location, we lack an intrinsic understanding as outbreak locations shift and evolve. Here, we fill this gap by developing a country distance approach to capture the pandemic’s propagation backbone tree from a complex airline network with multiple and evolving outbreak locations. We apply this approach, which is analogous to the effective resistance in series and parallel circuits, to examine countries’ closeness regarding disease spreading and evaluate the effectiveness of travel restrictions on delaying infections. In particular, we find that 63.2% of travel restrictions implemented as of 1 June 2020 are ineffective. The remaining percentage postponed the disease arrival time by 18.56 days per geographical area and resulted in a total reduction of 13,186,045 infected cases. Our approach enables us to design optimized and coordinated travel restrictions to extend the delay in arrival time and further reduce more infected cases while preserving air travel.

尽管在预测新型冠状病毒 (COVID-19) 大流行的时空模式和从初始爆发地点的数据开始量化非药物干预的有效性方面有许多成功的方法，但随着爆发地点的变化和演变，我们缺乏内在的理解. 在这里，我们通过开发一种国家距离方法来填补这一空白，以从具有多个且不断变化的爆发地点的复杂航空公司网络中捕获大流行的传播主干树。我们应用这种类似于串联和并联电路中的有效电阻的方法来检查各国在疾病传播方面的紧密程度，并评估旅行限制对延迟感染的有效性。特别是，我们发现 63。截至 2020 年 6 月 1 日实施的旅行限制中有 2% 无效。剩余的百分比将每个地理区域的疾病到达时间推迟了 18.56 天，从而总共减少了 13,186,045 例感染病例。我们的方法使我们能够设计优化和协调的旅行限制，以延长到达时间的延迟并进一步减少更多感染病例，同时保留航空旅行。