The impact of the psychological effect of infectivity on Nash-balanced control strategies for epidemic networks

The psychological effect of epidemic-related fear, guilt, or responsibility can change an individual’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 networks 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.