title: Suppression of epidemic spreading in complex networks by local information based behavioral responses.
abstract: The interplay between individual behaviors and epidemic dynamics in complex networks is a topic of recent interest. In particular, individuals can obtain different types of information about the disease and respond by altering their behaviors, and this can affect the spreading dynamics, possibly in a significant way. We propose a model where individuals' behavioral response is based on a generic type of local information, i.e., the number of neighbors that has been infected with the disease. Mathematically, the response can be characterized by a reduction in the transmission rate by a factor that depends on the number of infected neighbors. Utilizing the standard susceptible-infected-susceptible and susceptible-infected-recovery dynamical models for epidemic spreading, we derive a theoretical formula for the epidemic threshold and provide numerical verification. Our analysis lays on a solid quantitative footing the intuition that individual behavioral response can in general suppress epidemic spreading. Furthermore, we find that the hub nodes play the role of "double-edged sword" in that they can either suppress or promote outbreak, depending on their responses to the epidemic, providing additional support for the idea that these nodes are key to controlling epidemic spreading in complex networks.
authors.affiliation: School of Mathematical Science, Anhui University, Hefei 230039, China.
authors.forename: Hai-Feng
authors.initials: HF
authors.lastname: Zhang
authors.affiliation: Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China.
authors.forename: Jia-Rong
authors.initials: JR
authors.lastname: Xie
authors.affiliation: Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Tang
authors.affiliation: School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA.
authors.forename: Ying-Cheng
authors.initials: YC
authors.lastname: Lai
mesh_terms: D003140:Communicable Disease Control / Q000379:methods / Q000706:statistics & numerical data*; D003141:Communicable Diseases / Q000453:epidemiology*; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000517:prevention & control* / Q000706:statistics & numerical data*; D015438:Health Behavior; D006801:Humans; D015994:Incidence; D033181:Information Dissemination / Q000379:methods; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D012307:Risk Factors; D012944:Social Support