Background Restrictions on social interaction and movement were implemented by the German government in March 2020 to reduce the transmission of coronavirus disease 2019 (COVID-19). Apple's “Mobility Trends” (AMT) data details levels of community mobility; it is a novel resource of potential use to epidemiologists.

Objective The aim of the study is to use AMT data to examine the relationship between mobility and COVID-19 case occurrence for Germany. Is a change in mobility apparent following COVID-19 and the implementation of social restrictions? Is there a relationship between mobility and COVID-19 occurrence in Germany?

Methods AMT data illustrates mobility levels throughout the epidemic, allowing the relationship between mobility and disease to be examined. Generalized additive models (GAMs) were established for Germany, with mobility categories, and date, as explanatory variables, and case numbers as response.

Results Clear reductions in mobility occurred following the implementation of movement restrictions. There was a negative correlation between mobility and confirmed case numbers. GAM using all three categories of mobility data accounted for case occurrence as well and was favorable (AIC or Akaike Information Criterion: 2504) to models using categories separately (AIC with “driving,” 2511. “transit,” 2513. “walking,” 2508).

Conclusion These results suggest an association between mobility and case occurrence. Further examination of the relationship between movement restrictions and COVID-19 transmission may be pertinent. The study shows how new sources of online data can be used to investigate problems in epidemiology.

背景技术 德国政府于2020年3月对社交互动和活动进行了限制，以减少2019年冠状病毒疾病（COVID-19）的传播。苹果的“移动趋势”（AMT）数据详细说明了社区的移动水平；对于流行病学家来说，这是一种潜在的新资源。

目的 研究的目的是使用AMT数据来检查德国的流动性与COVID-19病例发生之间的关系。在COVID-19和社会限制措施实施之后，流动性变化是否明显？在德国，流动性和COVID-19发生率之间是否有关系？

方法 AMT数据说明了整个流行病的流动性水平，可以检查流动性与疾病之间的关系。为德国建立了通用的加性模型（GAM），其流动性类别和日期为解释变量，案例编号为响应。

结果 实施行动限制后，行动能力明显下降。流动性与确诊病例数之间呈负相关。使用所有三个类别的流动性数据的GAM也会导致案例的发生，并且对于单独使用类别的模型（AIC的“驾驶”，2511。“公交”，2513。“步行”，AIC或Akaike信息准则：2504）有利。 2508）。

结论 这些结果表明流动性与病例发生之间存在关联。可能需要进一步检查运动限制与COVID-19传播之间的关系。该研究表明如何使用新的在线数据源来调查流行病学问题。