Collaborated risk perception against multiple epidemics in a multiplayer network

The concurrent outbreak of epidemics has become a great threat to public health. An important problem is how individuals can protect themselves as the mandated protective measures are relaxed. Previous studies developed various models to investigate the correlated spreading dynamics of concurrent epidemics and the protective measures against them. However, a critical oversight remains that people’s risk perceptions on multiple epidemics are also correlated or even collaborated. In this paper, we build an SS-IS-SI-II coupled model in a multilayer network to describe two concurrent epidemics, integrating collaborated risk perception and spontaneous social distancing as individuals’ self-protection. Moreover, an adjustable coefficient is proposed to describe different levels of inter-epidemic correlations (competition/independence/cooperation). It is found that increasing the levels of inter-epidemic correlation will increase the infected density of both epidemics. Collaborated risk perception is generally more effective in reducing infections across different levels of inter-epidemic correlation, compared with independent risk perception. But its effect is dependent. For one epidemic, when its infectivity is very high or the infectivity of the other epidemic is very low, the effect of collaborated risk perception will be largely reduced. Based on the model, we further investigate the minimum social distancing required to contain epidemics under different conditions, and the priority on different layers is also explored. This research extends existing literature on co-evolutional epidemic dynamics, and lay a foundation to model correlated risk perceptions against epidemics. The results provide implications for individuals to take self-protection against concurrent epidemics.