The Effect of News Dissemination on Infection Dynamics: An Evolutionary Epidemic Model in a Network Setting

This paper delves into the ramifications of unofficial news dissemination on the spread of pandemics, aiming to gauge its influence on contagion levels, a pivotal factor in pandemic surveillance. To assess and regulate contagion, we’ve adapted a traditional SIR model, incorporating informed citizens exposed to both official and unofficial information streams. Our methodology combines conventional analysis with an evolutionary game framework, wherein the ratio of individuals correctly informed through authentic pandemic news evolves over time. We posit that exposure to distinct news types influences participants’ behavior, thereby impacting contagion rates and the acceptance of official versus unofficial news. Our findings underscore the necessity for swift governmental action against unofficial news upon pandemic declaration to curb infection spikes. This study underscores the imperative for stringent measures to combat unofficial news during initial panic stages, as well as the significance of beliefs and collective coordination post-pandemic control. Our model accounts for virus spread in a scale-free network, where an ongoing evolutionary game unfolds among network neighbours. The model’s control parameter is the flow of pandemic information, and we present the structure of optimal control alongside numerical simulations to illustrate our findings.