Abstract
In this paper, we propose a nonlinear coupled model to study the two interacting processes of awareness diffusion and epidemic spreading on the same individual who is affected by different neighbor behavior status on multiplex networks. We achieve this topology scenario by two kinds of factors, one is the perception factor that can change interplay between different layers of networks and the other is the neighbors’ behavior status that can change the infection rate in each layer. According to the microscopic Markov chain approach (MMCA), we analyze the dynamical evolution of the system and derive the theoretical epidemic threshold on uncorrelated heterogeneous networks, and then, we validate the analysis by numerical simulation and discuss the final size of awareness diffusion and epidemic spreading on a scale-free network. With the outbreak of COVID-19, the spread of epidemic in China prompted drastic measures for transmission containment. We examine the effects of these interventions based on modeling of the awareness-epidemic and the COVID-19 epidemic case. The results further demonstrate that the epidemic spreading can be affected by the effective transmission rate of the awareness and neighbors’ behavior status.
Highlights
Introduction e outbreak of COVID19 can involve the diffusion of information about the epidemic, including the officially released authoritative information, rumors, and fears [1–3]
We assume that the health of awareness neighbors plays different roles in the formation of node self-awareness and propose the health-impact-awareness status factors (∆ρ1, ∆ρ2, and ∆ρ3) to distinguish this effect in the awareness layer; the heterogeneity of neighbors degree distribution will affect the individual’s epidemic infection rate, and the behavior-impact-epidemic status factor (1 − e(− kj/ k), a positive correlation function of neighbors degree) is established to discriminate the difference in the epidemic layer. erefore, we propose a multiplex networks model to comprehend the spreading dynamics between epidemic and awareness on the same population with the influence of different neighbor behavior status
On the one hand, we discuss the interaction between awareness diffusion and epidemic spreading; on the other hand, we analyze the influence of neighbor status on epidemic spreading when awareness diffusion and neighbor behavior are coupled in multiple networks
Summary
Aware (A) individuals can apply their awareness to decrease the probability of being infected. Erefore, we assume that unaware infected (UI) and unaware recovered (UR) individuals know about the epidemic and can increase the probability of being aware with the epidemic perception factor f2. (ii) Epidemic spreading in layer 2: the spreading of epidemic satisfies the SIR (susceptible-infected-recovered) process. Aware susceptible (AS) individuals can apply the awareness to decrease the probability of being infected with the awareness perception factor f1. According to these presumptions, there are six primary states: US (unawareness susceptibility); UI (unawareness infection); UR (unawareness recovered); AS (awareness susceptibility); AI (awareness infected); and Parameter β μ λ δ f1 f2 ∆ρ1, ∆ρ2, and ∆ρ3 (1 − e− (kj/ k)). Description Probability of getting infected for susceptible individuals (the basic infection rate). AR UR (ii) Epidemic spreading US + I ⟶β, 1 + (1 − e− kj/ k) k))AI + I, AS + I ⟶f1 ∗ β, 1 + (1 − e− kj/ k) 1 + (1 − e− (kj/ k))AI + I
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
