Spatial networks and the spread of COVID-19: results and policy implications from Germany

Abstract Spatial networks are known to be informative about the spatiotemporal transmission dynamics of COVID-19. Using district-level panel data from Germany that cover first 22 weeks 2020, we show mobility, commuter and social all predict propagation epidemic. The main innovation our approach is it incorporates whole network updated information on case numbers across districts over time. We find when disease incidence increases in neighbouring regions, home district surge one week later. magnitude these effects comparable within-district transmission, illustrating importance as drivers local dynamics. After introduction containment policies mid-March, intensity drops substantially. Our analysis suggests this reduction primarily due a change quality—not quantity—of interregional movements. This implies blanket mobility restrictions not prerequisite for containing spread