Dirk Brockmann, Christof Schütte, Sarah Wolf
Humboldt-Universität zu Berlin, Insitute of Biology, Theoretical Biophysics
Infectious disease outbreaks challenge societies by creating dynamic stochastic infection networks between human individuals in geospatial and demographical contexts. We develop a comprehensive agent-based model that includes the clinically described stages of infection, disease and recovery for COVID-19. It incorporates demographic data, realistic daily schedules, and the physical location of individuals. We comparatively simulate and analyze different non-pharmaceutical intervention scenarios and exit strategies, depending on the location (e.g., workplace, school, public places such as shopping malls, etc.) but also on the actual time of day. We integrated a large amount of publicly available data, e.g., on locations, age distribution, household composition, daily occupation, geographical information, and sociological data for typical numbers and types of social contacts in the population. Applied to concrete communities, the R value and realistic infection networks are emergent properties of the model.
Dynamic networks, stochasticity, spatio-temporal infection spreading, individual behavior and demographic data