Ponce-de-Leon Miguel, Pontes Camila, Arenas Alex, Valencia Alfonso
Barcelona Supercomputing Center, Barcelona, Spain.
Universitat Rovira i Virgili, Tarragona, Spain.
Sci Rep. 2025 Aug 26;15(1):31504. doi: 10.1038/s41598-025-17218-4.
Human mobility played a key role in shaping the spatiotemporal dynamics of COVID19 transmission. This study employs Transfer Entropy (TE), an information-theoretic approach, to investigate the directional relationship between interregional mobility and COVID19 spread in Spain. Specifically, we use the mobility-associated risk time series, derived from phone-based origin-destination data and local infection prevalence, to estimate the flow of potentially infected individuals between regions. TE is then applied to measure the information flow from mobility-associated risk to regional case counts, enabling us to uncover spatio-temporal patterns of mobility-driven transmission. Using real-world data, we identified provinces that acted as outbreak drivers during the COVID19 pandemic in Spain and detected temporal shifts in the strength and direction of mobility's influence. Our findings align with key epidemiological events, such as the 2020 summer outbreak in Lleida linked to seasonal workers, and highlight the effects of non-pharmaceutical interventions, including bar closures in Catalunya, on transmission dynamics. Finally, we validated our approach using simulations from a metapopulation SIR model with known transmission pathways, showing that TE can recover mobility-induced transmission structure while reducing indirect or spurious associations. Altogether, our work provides a novel approach to study the effect of interregional mobility on epidemic spread and to uncover spatio-temporal patterns of mobility-driven transmission, offering valuable insights to inform the timing and regional targeting of non-pharmaceutical interventions.
人员流动在塑造新冠病毒传播的时空动态方面发挥了关键作用。本研究采用转移熵（TE）这一信息论方法，来探究西班牙区域间流动与新冠病毒传播之间的方向性关联。具体而言，我们利用从基于手机的出行起终点数据和当地感染率得出的与流动相关的风险时间序列，来估计各地区之间潜在感染个体的流动情况。然后应用转移熵来衡量从与流动相关的风险到区域病例数的信息流，使我们能够揭示由流动驱动的传播的时空模式。利用实际数据，我们确定了在西班牙新冠疫情期间充当疫情驱动因素的省份，并检测到流动影响的强度和方向的时间变化。我们的研究结果与关键的流行病学事件相符，比如2020年莱里达与季节性工人相关的夏季疫情爆发，并突出了包括加泰罗尼亚酒吧关闭在内的非药物干预措施对传播动态的影响。最后，我们使用具有已知传播途径的异质种群SIR模型的模拟对我们的方法进行了验证，结果表明转移熵能够恢复由流动引起的传播结构，同时减少间接或虚假关联。总体而言，我们的工作提供了一种新颖的方法来研究区域间流动对疫情传播的影响，并揭示由流动驱动的传播的时空模式，为非药物干预措施的时机选择和区域靶向提供了有价值的见解。
