Socio-spatial diffusion of COVID-19 in Germany

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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).