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We present an agent-based epidemiological model that is based on an agent-based model for traffic and mobility. The model consists of individual agents that follow individual daily activity plans, which include, for each activity, locations, start times, and end times. Evidently, one can place a virus spreading dynamic on top of this, by infecting one or more agents, and then track the resulting virus dynamics through the model. Normally, the model is used to investigate non-pharmaceutical interventions. In the present paper, we undertake steps to better understand the infection graph. It becomes clear that the typical infection graph representation that connects individual people is an even more expensive representation than our original, already expensive data-driven mobility model. We then undertake first steps towards analyzing the model with respect to a possible percolation transition.

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. Results show that in Berlin (Germany), behavioral changes of the population mostly happened before the government-initiated so-called contact ban came into effect. Also, the model is used to determine differentiated changes to the reinfection rate for different interventions such as reductions in activity participation, the wearing of masks, or contact tracing followed by quarantine-at-home. One important result is that successful contact tracing reduces the reinfection rate by about 30 to 40%, and that if contact tracing becomes overwhelmed then infection rates immediately jump up accordingly, making rather strong lockdown measures necessary to bring the reinfection rate back to below one.