Modeling Exit Strategies from COVID-19 Lockdown with a Focus on Antibody Tests

Reinhard German; Anatoli Djanatliev; Lisa Maile; Peter Bazan; Holger Hackstein

A further state chart represents the disease states of agents. Beginning by susceptibles, the statechart shows a behavior which is very close to the conceptual SEIR model of Sec. 3. By configuring the daily capacity for antibody tests, they can be applied following one of the defined strategies in Sec. 3 . As the ABS model differentiates between working groups and other individual attributes, an even more precise testing strategy can be evaluated. For example, hospital staff and system-relevant workers can be tested with a higher priority. Input configuration allows to preset a sensitivity and a specificity value for the test. For a more precise analysis, it is even possible to consider different error probabilities for agents that are affected and have not been recovered yet. Fig. 3 shows a screenshot of the model with 20.000 agents, the agents are shown as icons in their environments, colors represent their states. The model can deal with several ten thousand agents easily and is ready to study effects in a region, but differentiated for the agent groups. It is for instance possible to derive figures of health personnel with ABs. It will also easily be possible to include other important aspects into the ABS model: age-dependent severity and CFRs, controlled change of social status (from reduced to normal) depending on vulnerability (e.g., elderly and other risk groups), viral load at infections (can influence severity), etc. Resource-based analysis can show, if enough beds, hospital staff, or system-relevant workers are available when the pandemic gets more severe. It would also be interesting to model regional clusters with ABS and connect them via continuous flows or to provide an SD environment with the general disease dynamics for the ABS model of a region, similar as in [5] .