论文标题
挖掘Google和Apple移动性数据：COVID-19社会距离的时间解剖结构
Mining Google and Apple mobility data: Temporal Anatomy for COVID-19 Social Distancing
论文作者
论文摘要
我们采用Google和Apple移动性数据来识别，量化和分类不同程度的社会疏远程度，并在欧洲和美国的Covid-19大流行中的第一波浪潮中表征其烙印。我们独立于政治决策，确定通过Google和Apple数据颁布的社会疏远时期。有趣的是，我们观察到在欧洲国家和美国州的流动性降低两到五周后，感染率的总体下降。
We employ the Google and Apple mobility data to identify, quantify and classify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Apple data, independently from the political decisions. Interestingly we observe a general decrease in the infection rate occurring two to five weeks after the onset of mobility reduction for the European countries and the American states.
