Socio-spatial diffusion of COVID-19 in Germany

可以说，与以往任何其他大流行病不同的是，COVID-19得到了详细的监测和绘图，从而能够进行精细的分析。到目前为止，COVID-19大流行的轨迹遵循以往大流行的非线性轨迹，呈波浪形，意味着加速和减速阶段。我们认为，有未使用的潜力，利用时空数据来了解大流行的爆发。因此，本研究项目旨在通过过程视角分析德国COVID-19大流行的轨迹，以检测扩散的时空模式。为此，我们提出了三个分析步骤。首先，我们开发了一个阶段模型，通过考虑国家规模的指标（例如，发病率或死亡率的空间自相关）和区域尺度上的指示符（例如，热点和冷点）。其次，我们对小尺度时空子集或“轨迹窗口”（例如，大流行病在特定时间段内在特定地区如何发展）。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。通过分析哪种类型的传播模式在大流行的哪个阶段是相关的，我们的目标是根据不断变化的条件（例如，新菌株或感染控制策略）。通过这一项目有助于扩散理论，并旨在提供政策建议，直接解决空间扩散的政策（例如，边境关闭）。

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).