Non-Pharmaceutical Interventions (NPIs), such as social gathering restrictions, have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of people. To support policy-makers, multiple studies have first modeled human mobility via macro indicators (e.g., average daily travel distance) and then studied the effectiveness of NPIs. In this work, we focus on mobility modeling and, from a micro perspective, aim to predict locations that will be visited by COVID-19 cases. Since NPIs generally cause economic and societal loss, such a micro perspective prediction benefits governments when they design and evaluate them. However, in real-world situations, strict privacy data protection regulations result in severe data sparsity problems (i.e., limited case and location information). To address these challenges, we formulate the micro perspective mobility modeling into computing the relevance score between a diffusion and a location, conditional on a geometric graph. we propose a model named Deep Graph Diffusion Infomax (DGDI), which jointly models variables including a geometric graph, a set of diffusions and a set of locations.To facilitate the research of COVID-19 prediction, we present two benchmarks that contain geometric graphs and location histories of COVID-19 cases. Extensive experiments on the two benchmarks show that DGDI significantly outperforms other competing methods.

翻译：社会集会限制等非药用干预(NPI)显示,通过减少人们的接触,能够有效减缓COVID-19的传播。为了支持决策者,多项研究首先通过宏观指标(例如平均每天旅行距离)模拟人类流动,然后研究NPI的效力。在这项工作中,我们侧重于流动建模,从微观角度出发,目的是预测COVID-19案例将访问的地点。由于NPI通常造成经济和社会损失,因此微观视角预测有利于政府设计和评估这些变量。然而,在现实世界形势下,严格的隐私数据保护条例导致严重的数据紧张问题(例如,有限的案例和地点信息)。为了应对这些挑战,我们制定微观视角移动模型,计算扩散和地点之间的相关得分,并以几何图为条件。我们提议了一个名为“深图Digal Difluculation Infomeax(DGDDI)”的模型,共同模拟变量,包括一个几何图表、一套传播图和一套地点组合。便利对CVI-19号数据进行严格的数据保护条例的研究,从而大大地展示了COVI-19号基准。