Luca Massimiliano, Lepri Bruno, Frias-Martinez Enrique, Lutu Andra
Bruno Kessler Foundation, Trento, Italy.
Free University of Bolzano, Bolzano, Italy.
EPJ Data Sci. 2022;11(1):22. doi: 10.1140/epjds/s13688-022-00335-9. Epub 2022 Apr 4.
Most of the studies related to human mobility are focused on intra-country mobility. However, there are many scenarios (e.g., spreading diseases, migration) in which timely data on international commuters are vital. Mobile phones represent a unique opportunity to monitor international mobility flows in a timely manner and with proper spatial aggregation. This work proposes using roaming data generated by mobile phones to model incoming and outgoing international mobility. We use the gravity and radiation models to capture mobility flows before and during the introduction of non-pharmaceutical interventions. However, traditional models have some limitations: for instance, mobility restrictions are not explicitly captured and may play a crucial role. To overtake such limitations, we propose the COVID Gravity Model (CGM), namely an extension of the traditional gravity model that is tailored for the pandemic scenario. This proposed approach overtakes, in terms of accuracy, the traditional models by 126.9% for incoming mobility and by 63.9% when modeling outgoing mobility flows.
大多数与人员流动相关的研究都集中在国内流动上。然而，在许多情况下（如疾病传播、移民），国际通勤者的及时数据至关重要。手机为及时监测国际流动情况并进行适当的空间汇总提供了独特的机会。这项工作提出使用手机产生的漫游数据来模拟出入境国际流动情况。我们使用引力模型和辐射模型来捕捉在实施非药物干预措施之前和期间的流动情况。然而，传统模型存在一些局限性：例如，流动限制没有被明确捕捉到，而这可能起着关键作用。为了克服这些局限性，我们提出了新冠引力模型（CGM），即一种针对疫情情况量身定制的传统引力模型的扩展。就准确性而言，这种提出的方法在模拟入境流动时比传统模型高出126.9%，在模拟出境流动时高出63.9%。
