  Countries around the world implement nonpharmaceutical interventions (NPIs)
to mitigate the spread of COVID-19. Design of efficient NPIs requires
identification of the structure of the disease transmission network. We here
identify the key parameters of the COVID-19 transmission network for time
periods before, during, and after the application of strict NPIs for the first
wave of COVID-19 infections in Germany combining Bayesian parameter inference
with an agent-based epidemiological model. We assume a Watts-Strogatz
small-world network which allows to distinguish contacts within clustered
cliques and unclustered, random contacts in the population, which have been
shown to be crucial in sustaining the epidemic. In contrast to other works,
which use coarse-grained network structures from anonymized data, like cell
phone data, we consider the contacts of individual agents explicitly. We show
that NPIs drastically reduced random contacts in the transmission network,
increased network clustering, and resulted in a change from an exponential to a
constant regime of newcases. In this regime, the disease spreads like a wave
with a finite wave speed that depends on the number of contacts in a nonlinear
fashion, which we can predict by mean field theory. Our analysis indicates that
besides the well-known transitionbetween exponential increase and exponential
decrease in the number of new cases, NPIs can induce a transition to another,
previously unappreciated regime of constant new cases.
