FACULTY AND RESEARCH

The psychological effect of epidemic-related fear, guilt, or responsibility can change an indi- vidual’s behavior and influence the diffusion of an infectious disease in a population. The individual-based network approach allows for implementing a nonlinear infection activation on each node’s ego-network to model the psychological effect of infectivity. In a setting of two subpopulations on a connected network, with GDP-differentiation, each policymaker deploys their optimal cost-efficient control strategy depending on their interlinked outcomes. The Nash equilibrium of the related matrix game is shown to vary over a range of calibrated generalized logistic infection activations. Simulations on realistically designed contact net- works with heterogeneous populations show that the behavior from the corresponding range of psychological effects will impact the severity and the oscillatory characteristics of the SEIRSD-epidemic evolution. The nonlinear characteristic and the specific parametrization of the infection activation function prove a decisive factor in the severity and evolution of the epidemic and in the policymaker’s decision process for the Nash-optimal control strategy.