Agent-based modeling to estimate impact of parameters like events on a pandemic exemplified on Sars-CoV-2

In actual pandemic situations like COVID-19 it is important to understand the influence of single mitigation measures as well as combinations to create most dynamic impact for lockdown scenarios. Therefore we created an Agent-based model (ABM) to simulate the spread of Sars-CoV-2 in an abstract city model with several types of places and agents. In comparison to infection numbers in Germany our ABM could be shown to behave similarly over the first wave. In our model we implemented the possibility to test the effectiveness of mitigation measures and lockdown scenarios on the course of the pandemic. Thereby we focused on parameters of local events as possible mitigation measure and ran simulations including varying size, duration,frequency and the proportion of events. Most changes of single event parameters, except of frequency showed only a small influence on the overall course of the pandemic. By applying different lockdown scenarios in our simulations we could observe drastic changes in the number of infections per day and depending on the lockdown strategy even an delayed peak in infection numbers of the second wave. As an advantage of the developed ABM it is possible to analyse the individual risk of single agents during the pandemic. By this in contrast to standard or adjusted ODEs we could observe an 21% (with masks) / 48% without masks increased risk for single reappearing participants on local events with a linearly increasing risk based on the length of the events.