ArticleJournal of statistical physics2013
Epidemics in adaptive social networks with temporary link deactivation.
Article in Journal of statistical physics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- Adaptive network modeling of social distancing interventions.Journal of theoretical biology · 2022Article
- Balancing Quarantine and Self-Distancing Measures in Adaptive Epidemic Networks.Bulletin of mathematical biology · 2022Article
- A model for the co-evolution of dynamic social networks and infectious disease dynamics.Computational social networks · 2021Article
- Edge-Based Compartmental Modelling of an SIR Epidemic on a Dual-Layer Static-Dynamic Multiplex Network with Tunable Clustering.Bulletin of mathematical biology · 2018Article
- Bursting endemic bubbles in an adaptive network.Physical review. E · 2018Article
- Epidemic dynamics on information-driven adaptive networks.Chaos, solitons, and fractals · 2018Article
- Dynamics of epidemic diseases on a growing adaptive network.Scientific reports · 2017Article
- Stochastic Models of Emerging Infectious Disease Transmission on Adaptive Random Networks.Computational and mathematical methods in medicine · 2017Article
- Epidemics with temporary link deactivation in scale-free networks.Journal of physics. A, Mathematical and theoretical · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Disease spread in a society depends on the topology of the network of social contacts. Moreover, individuals may respond to the epidemic by adapting their contacts to reduce the risk of infection, thus changing the network structure and affecting future disease spread. We propose an adaptation mechanism where healthy individuals may choose to temporarily deactivate their contacts with sick individuals, allowing reactivation once both individuals are healthy. We develop a mean-field description of this system and find two distinct regimes: slow network dynamics, where the adaptation mechanism simply reduces the effective number of contacts per individual, and fast network dynamics, where more efficient adaptation reduces the spread of disease by targeting dangerous connections. Analysis of the bifurcation structure is supported by numerical simulations of disease spread on an adaptive network. The system displays a single parameter-dependent stable steady state and non-monotonic dependence of connectivity on link deactivation rate.
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Registered trials
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.