Co-evolution dynamics of epidemic and information under dynamical multi-source information and behavioral responses

Abstract

In the absence of effective treatment programs and limited medical resources, multi-source information dynamically evolves with an epidemic and motivates people to adopt behavioral responses, which contributes much to reducing their infection risk and suppressing the epidemic spread. Here, we aim at studying the effects of dynamical multi-source information and behavioral responses on the co-evolution of epidemic and information in time-varying multiplex networks. We propose the UAU-SIS (Unaware–Aware–Unaware– Susceptible–Infected–Susceptible) model with time-varying self-awareness and behavioral responses. Under the framework of time-varying multiplex networks and with a Microscopic Markov Chain Approach (MMCA), we analytically derive the epidemic thresholds for the proposed model. Experimental results for artificial networks show that time-varying behavioral responses can effectively suppress the epidemic spread with an increased epidemic threshold, while time-varying self-awareness can only reduce the scale of epidemic spread. In addition, the role of dynamical multi-source information in suppressing epidemic spread is limited. When the information transmission rate is beyond a certain critical value or the information efficiency is low, it will no longer affect the epidemic spread. Detailed analysis on the co-evolution of epidemic and information has to consider the heterogeneity of individuals in obtaining multi-source information and taking behavioral responses. Only when many people can obtain multi-source information and take behavioral responses, time-varying self-awareness and behavioral responses have a great impact on suppressing epidemic spread. Furthermore, we apply our proposed framework to two typical real-world networks and find that the results on real-world networks are consistent with those on artificial networks. Thus, the proposed method is expected to provide helpful guidance for coping with the COVID-19 or future emerging epidemics.