Dynamic analysis and decision-making in disease-behavior systems with perceptions

Disease-behavior systems focus on the feedback loop between disease prevalence and individual vaccinating behavior: prevalent diseases stimulate individuals to vaccinate to avoid infection, high vaccination coverage mitigates the spread of diseases, then payoff-maximizers prefer not to vaccinate, which leads to the increase of non-vaccinators and facilitates disease outbreaks. In such coupled systems, individual vaccinating behavior usually depends on the perceived rather than real payoffs of infection and vaccination, which has not been fully explored. In this paper, we study the dynamics of disease-behavior systems and associated economic costs under perceived payoffs. We consider two factors affecting such perceived payoffs: the population structure on which information and diseases spread, and individuals' capabilities of processing information. They are modeled by network and prospect theory, respectively. Specifically, the population structure is described by a two-layer network composed of the decision-making network and the infection contagion network. We find network characteristics, such as network diameter, degree heterogeneity, and clustering, do not influence disease-behavior systems. On the other hand, taking local information from neighbors into account during the decision-making process and increasing the availability of vaccination raise the equilibrium level of vaccination. In addition, lowering the average degree of the infection contagion network (i.e., reducing physical contacts in the target population) suppresses the spread of diseases. All the three interventions reduce the costs of populations.