论文标题
使用漫游手机轨迹在Covid-19大流行期间使用漫游手机轨迹进行建模
Modeling International Mobility using Roaming Cell Phone Traces during COVID-19 Pandemic
论文作者
论文摘要
与人类流动性有关的大多数研究都集中在国内活动上。但是，有许多情况（例如，传播疾病，迁移），其中及时的国际通勤者数据至关重要。手机代表了及时监视国际移动性流动的独特机会，并通过适当的空间汇总。这项工作建议使用手机生成的漫游数据来建模传入和即将推出的国际移动性。我们使用重力和辐射模型来捕获非药物干预措施之前和期间的迁移率。但是，传统模型有一些局限性：例如，没有明确捕获移动性限制，并且可能起着至关重要的作用。为了实现这种局限性，我们提出了共同的重力模型（CGM），即针对大流行情景量身定制的传统重力模型的扩展。在准确性方面，这种方法超过了传统模型的传统模型，即传统的移动性，在建模传出的移动性流动时，这种模型超过了63.9％。
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.
