Volume 114, Number 3, May 2016
|Number of page(s)||6|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||01 June 2016|
Impact of asymptomatic infection on coupled disease-behavior dynamics in complex networks
1 School of Mathematical Science, Anhui University - Hefei 230601, China
2 Center of Information Support & Assurance Technology, Anhui University - Hefei, 230601, China
3 School of Physics and Material Science, Anhui University - Hefei 230601, China
4 Department of Communication Engineering, North University of China - Taiyuan, Shan'xi 030051, China
5 Department of Modern Physics, University of Science and Technology of China - Hefei 230026, China
6 School of Mathematics and Statistics, The University of Western Australia Crawley, Western Australia 6009, Australia
7 Mineral Resources, CSIRO - Kensington, Western Australia, 6151, Australia
Received: 5 March 2016
Accepted: 19 May 2016
Studies on how to model the interplay between diseases and behavioral responses (so-called coupled disease-behavior interaction) have attracted increasing attention. Owing to the lack of obvious clinical evidence of diseases, or the incomplete information related to the disease, the risks of infection cannot be perceived and may lead to inappropriate behavioral responses. Therefore, how to quantitatively analyze the impacts of asymptomatic infection on the interplay between diseases and behavioral responses is of particular importance. In this letter, under the complex network framework, we study the coupled disease-behavior interaction model by dividing infectious individuals into two states: U-state (without evident clinical symptoms, labelled as U) and I-state (with evident clinical symptoms, labelled as I). A susceptible individual can be infected by U- or I-nodes, however, since the U-nodes cannot be easily observed, susceptible individuals take behavioral responses only when they contact I-nodes. The mechanism is considered in the improved Susceptible-Infected-Susceptible (SIS) model and the improved Susceptible-Infected-Recovered (SIR) model, respectively. Then, one of the most concerned problems in spreading dynamics: the epidemic thresholds for the two models are given by two methods. The analytic results quantitatively describe the influence of different factors, such as asymptomatic infection, the awareness rate, the network structure, and so forth, on the epidemic thresholds. Moreover, because of the irreversible process of the SIR model, the suppression effect of the improved SIR model is weaker than the improved SIS model.
PACS: 87.23.Cc – Population dynamics and ecological pattern formation / 89.75.Hc – Networks and genealogical trees
© EPLA, 2016
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