Socio-spatial diffusion of COVID-19 in Germany

可以说，与之前的任何一次大流行不同，COVID-19得到了详细监测和绘制，从而能够进行细粒度分析。到目前为止，2019冠状病毒病大流行的轨迹遵循以往大流行的非线性轨迹，其波动模式意味着加速和减速阶段。我们认为，在利用时空数据了解大流行暴发方面存在未被利用的潜力。因此，本研究项目旨在通过过程视角分析新冠肺炎疫情在德国的传播轨迹，检测疫情扩散的时空格局。为此，我们提出三个分析步骤。首先，我们开发了一个阶段模型，通过考虑国家尺度上的指标（如发病率或死亡率的空间自相关性）和区域尺度上的指标（如热点和冷点），将德国的大流行轨迹细分为不同阶段。其次，我们对小尺度的时间空间子集或“轨迹窗口”进行分类（例如，大流行在特定地区的特定时间框架内如何展开）。对于大流行的每个阶段，我们试图确定两到四种相关类型的轨迹窗口。第三，我们通过扩散理论的视角来探索前面步骤中确定的模式。通过分析在大流行的哪个阶段哪些类型的传播模式是相关的，我们的目标是根据不断变化的条件（例如，新的菌株或感染控制政策），深入了解传播的地理位置在大流行过程中如何变化。通过这种方式，该项目为扩散理论做出了贡献，并旨在就直接解决空间扩散（例如边境关闭）的政策提出政策建议。

Arguably unlike any other pandemic before, COVID-19 has been monitored and mapped in detail, enabling fine-grained analysis. So far, the trajectory of the COVID-19 pandemic followed a non-linear trajectory known from previous pandemics with a wave pattern implying phases of acceleration and deceleration. We argue that there is unused potential in using tempo-spatial data to understand pandemic outbreaks. Therefore, this research project aims to analyze the trajectory of the COVID-19 pandemic in Germany through a process perspective to detect spatio-temporal patterns of diffusion. To do so, we propose three analytical steps. First, we develop a stage model that subdivides the pandemic trajectory in Germany in different stages by considering indicators on the national scale (e.g., spatial autocorrelation of incidence or mortality) andindicators on the regional scale (e.g., hot spots and cold spots). Second, we classify small-scaled tempo-spatial subsets or "trajectory windows" (e.g., how the pandemic unfolded during a specific time frame in a specific region). For each stage of the pandemic we attempt to identify between two and four relevant types of trajectory windows. Third, we explore the patterns identified in the previous steps through the perspective of diffusion theory. By analyzing which types of diffusion patterns were relevant during which stage of the pandemic we aim to generate insights on how the geographies of spread vary over the course of the pandemic in light of changing conditions (e.g., new strains or infection control policies). Through this the project contributes to diffusion theory, and aims to give policy recommendations regarding policies that directly adress spatial diffusion (e.g., border closures).