Impact of Information Index–Driven Behavioral Dynamics and Social Dilemmas on Epidemic Control in Heterogeneous Social Networks

Source abstract

Information dissemination and underlying social structures profoundly influence human behavior and epidemic dynamics. This study introduces a compartmental framework, the SVAIRDM (susceptible–vaccinated–isolated–infected–recovered–deceased–information) model, which explicitly incorporates an information index to quantify the effects of public health information, including misinformation, on individual behavioral responses. The model is embedded within a two‐layer network architecture composed of Barabási–Albert (BA) scale‐free networks and Erdős–Rényi (ER) random networks, both of which capture the coevolution of disease transmission and information spread, reflecting the complex interplay observed in real‐world epidemics. Beyond epidemic dynamics, we examine the social dilemma associated with individual decision‐making in vaccination and isolation through the social efficiency deficit (SED) lens. Our analysis highlights how information‐driven behaviors shape collective welfare by quantifying the gap between socially optimal and individually rational outcomes. Interestingly, we observe instances of negative SED under certain conditions of the information index, suggesting that, paradoxically, intensified information dissemination may worsen collective outcomes despite improving individual‐level strategies. Our results show that the information index enhances epidemic control by facilitating earlier isolation and higher vaccination rates, with BA networks enabling rapid behavioral adaptation through highly connected hubs. In contrast, ER networks produce steadier, more uniform behavioral shifts. These findings underscore the importance of incorporating both behavioral–social dilemmas and information‐driven dynamics into epidemic modeling. This work provides a comprehensive framework for developing communication strategies that support vaccination and isolation policies and address the inefficiencies arising from social dilemmas, thereby offering more profound insights into optimizing epidemic mitigation in complex and densely connected populations.