论文标题
数据驱动的建模揭示了美国在美国共同流行期间对人类流动性的影响的影响
Data-Driven Modeling Reveals the Impact of Stay-at-Home Orders on Human Mobility during the COVID-19 Pandemic in the U.S
论文作者
论文摘要
延迟新型冠状病毒（Covid-19）传播的一种方法是通过实施旅行限制政策来减少人类旅行。目前尚不清楚这些政策在抑制流动性趋势的有效性是由于缺乏地面真理和描述大流行期间人类流动性的大规模数据集。这项研究使用了从匿名移动设备收集的基于位置的现实服务数据，以发现Covid-19期间的流动性变化，在美国的“全职”州订单下，该研究通过两个重要的指标来测量人类流动性：每人每日平均每人旅行数量和每日平均人数。数据驱动的分析和建模属性不到降低的旅行数量和人误的5％，从而传达出策略的影响。研究中开发的模型具有很高的预测准确性，可以应用于以经验验证的移动性趋势和支持时间敏感决策过程的流行模型。
One approach to delay the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. It is yet unclear how effective those policies are on suppressing the mobility trend due to the lack of ground truth and large-scale dataset describing human mobility during the pandemic. This study uses real-world location-based service data collected from anonymized mobile devices to uncover mobility changes during COVID-19 and under the 'Stay-at-home' state orders in the U.S. The study measures human mobility with two important metrics: daily average number of trips per person and daily average person-miles traveled. The data-driven analysis and modeling attribute less than 5% of the reduction in the number of trips and person-miles traveled to the effect of the policy. The models developed in the study exhibit high prediction accuracy and can be applied to inform epidemics modeling with empirically verified mobility trends and to support time-sensitive decision-making processes.
