ArticleChaos (Woodbury, N.Y.)2014
Suppression of epidemic spreading in complex networks by local information based behavioral responses.
Article in Chaos (Woodbury, N.Y.), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Behavioural change models for infectious disease transmission: a systematic review (2010-2015).Journal of the Royal Society, Interface · 2016Pooled it
- Assessing the impact of misinformation during the spread of infectious diseases.Scientific reports · 2025Article
- Co-evolution model of traffic travel and disease transmission under limited resources.Scientific reports · 2025Article
- Effects of official information and rumor on resource-epidemic coevolution dynamics.Journal of King Saud University. Computer and information sciences · 2022Article
- Risk perception and subsidy policy-based voluntary vaccination driven by multiple information sources.PloS one · 2022Article
- Influential Nodes Identification in Complex Networks via Information Entropy.Entropy (Basel, Switzerland) · 2020Article
- Effects of asymptomatic infection on the dynamical interplay between behavior and disease transmission in multiplex networks.Physica A · 2019Article
- Coevolution spreading in complex networks.Physics reports · 2019Review
- A stochastic SIR network epidemic model with preventive dropping of edges.Journal of mathematical biology · 2019Article
- Article
- Accurate ranking of influential spreaders in networks based on dynamically asymmetric link weights.Physical review. E · 2017Article
- Efficiency of prompt quarantine measures on a susceptible-infected-removed model in networks.Physical review. E · 2017Article
- How heterogeneous susceptibility and recovery rates affect the spread of epidemics on networks.Infectious Disease Modelling · 2017Article
- Use of social network analysis and global sensitivity and uncertainty analyses to better understand an influenza outbreak.Oncotarget · 2017Article
- Interplay between epidemic spread and information propagation on metapopulation networks.Journal of theoretical biology · 2017Article
- Effects of awareness diffusion and self-initiated awareness behavior on epidemic spreading - An approach based on multiplex networks.Communications in nonlinear science & numerical simulation · 2017Article
- Dynamics of epidemic diseases on a growing adaptive network.Scientific reports · 2017Article
- Impacts of complex behavioral responses on asymmetric interacting spreading dynamics in multiplex networks.Scientific reports · 2016Article
- Local immunization program for susceptible-infected-recovered network epidemic model.Chaos (Woodbury, N.Y.) · 2016Article
- Explosive Contagion in Networks.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.