Effects of asymptomatic infection on the dynamical interplay between behavior and disease transmission in multiplex networks

Abstract

h i g h l i g h t s• We study the impact of asymptomatic infection on the epidemic spread dynamics in multiplex networks.• We assume infected can be isolation and non isolation, then compare the research results of these two cases.• We take the individual heterogeneity into consideration and study whether it affect research results. a b s t r a c tMultiplex network theory is widely introduced to deepen the understanding of the dynamical interplay between self-protective behavior and epidemic spreading. Most of the existing studies assumed that all infected individuals can transmit diseaserelated information or can be perceived by their neighbors. However, owing to lack of distinct symptoms for patients in the initial stage of infection, the disease information cannot be transmitted in the population, which may lead to the wrong perception of infection risk and inappropriate behavior response. In this work, we divide infected individuals into Exposed-state (without obvious clinical symptoms) individuals and Infected-state (with evident clinical symptoms) individuals, both of whom can spread disease, but only Infected-state individuals can diffuse disease information. Then, in this work we establish UAU-SEIS (Unaware-Aware-Unaware-Susceptible-Exposed-Infected-Susceptible) model in multiplex networks and analyze the effect of asymptomatic infection and the isolation of Infected-state individuals on the density of infection and the epidemic threshold. Furthermore, we extend the UAU-SEIS model by taking the individual heterogeneity into consideration. Combined Markov chain approach and Monte-Carlo Simulations, we find that asymptomatic infection has an effect on the density of infected individuals and the epidemic threshold, and the extent of the effect is influenced by whether Infected-state individuals are isolated or treated. In addition, results show that the individual heterogeneity can lower the density of infected individuals, but cannot enhance the epidemic threshold.

Impacts of social distancing on the spread of infectious diseases with asymptomatic infection: A mathematical model

“…Previous studies usually assumed that self-protection would reduce people’s susceptibility to an infection [11] , [48] , [49] , [50] . We follow them and propose that spontaneous social distancing will decrease the node’s susceptibility to the infectious disease.…”

Associated Credit Risk Contagion with Incubatory Period: A Network-Based Perspective

“…ese literatures consider delay or incubatory period and study the propagation dynamics properties of virus including threshold, global stability, and immune strategy [41][42][43][44][45], which inspires us to study the credit risk contagion with incubatory period. Viviana and Lucia proposed a particular time-delay susceptible-infected-recovered model to investigate and describe the credit risk contagion in the market and studied the steady states according to different values of time delay and different bank support policies [46].…”

The coupled dynamics of information dissemination and SEIR-based epidemic spreading in multiplex networks

“…We have noticed that the epidemic spreading on the contact network and information dissemination on the information network are closely related and affect each other. For example, the outbreak of COVID-19 has caused the spread of COVID-19-related information among the population, which can trigger the public’s behavioral responses to reduce the risk of infection (e.g., wearing masks, reducing party activities, washing hands, disinfecting frequently and so on), and in turn impact on the epidemic spreading process [4] , [5] , [6] , [7] , [8] . Therefore, the researches on the interplay between the information dissemination and epidemic spreading have become an important topic in the field of transmission dynamics.…”

“…Among them, the multiplex networks is an interesting example of multilayer network setup [12] , [13] , in which the nodes represent the same entities in all layers, but the link relationship among nodes may be completely different [14] . Naturally, many scholars have begun to use the multiplex networks as the underlying framework to study the dynamical interplay of information dissemination and epidemic spreading [7] , [8] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] . Granell et al.…”

“…Shi et al. have proposed the UAU-SEIS model to analyze the dynamical interplay between behavior and epidemic spreading in multiplex networks, and considered the impact of individual heterogeneity on the disease transmission dynamics [8] .…”